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Some microeconometric evidence on the relationship between health and income

Some microeconometric evidence on the relationship between health and income This paper examines the association between income, income inequalities and health inequalities in Europe. The contribution of this paper is to study different hypotheses linking self-perceived health status and income, allowing for the identification of different mechanisms in income-related health inequalities. Using data from the Survey of Health, Ageing and Retirement in Europe (15 countries), we take the advantage of the cross-sectional and longitudinal nature of this rich database to make robust results. The analyses (coefficient estimates as well as average marginal effects) strongly support two hypotheses by showing that (i) income has a positive and concave effect on health (Absolute Income Hypothesis); (ii) income inequalities in a country affect all members in a society (strong version of the Income Inequality Hypothesis). However, our study suggests that, when considering the position of the individual in the income distribution, as well as the interaction between income inequalities and these rankings, one cannot identify individuals the most affected by income inequalities (which should be the least well-off in a society according to the weak version of the Income Inequality Hypothesis). Finally, the robustness of this study is emphasized when implementing a generalized ordered probit to consider the subjective nature of the self-perceived health status to avoid the traps encountered in previous studies. JEL Classification: IOO, I14, D31 Keywords: Health inequalities, Income inequalities, Self-reported health, Europe Background relationship between income and health. Higher incomes The last few years have seen unprecedented attention to can provide means for purchasing a better health sta- an attempt by policy makers, policy advisers and interna- tus. The second one is the strong version of the Income tional institutions to reduce health inequalities. To do so, Inequality Hypothesis and it asserts that the health sta- they usually focus on the access to healthcare, given that tus is determined by income inequalities within a society. such policies allow to improve the health of lower income Thus, the health of all individuals is affected by an increase groups [28, 34]. Improving equality of access to health- or a decrease in income inequalities. The last one, a care is however not the sole public policy which can favor weak version of the Income Inequality Hypothesis, says health equality. In particular, it has been widely said that that income inequalities are a threat to individuals placed income and income inequalities are associated to health at the lower end of the income distribution. This last status; thus, any public policy which influences income hypothesis implies that income inequalities do not impact and/or income inequalities might influence health. In this low income people and high income people in the same way, studying the relationship between income, income magnitude. inequalities and health is interesting per se. With these Various authors have studied the Absolute Income elements in mind, this paper confronts on an empir- Hypothesis mainly in the United States, using different ical basis three hypotheses. The first one, called the health measures, like self-perceived measures [26], life Absolute Income Hypothesis, was initially introduced by expectancy [10] and other health outcomes [8, 12]. Fiscella Preston [29] and states that there is a positive and concave and Franks [13], Kennedy et al. [20], Van Doorslaer et al. [32], Wagstaff et al. [33] focus on the strong version of *Correspondence: amelie.adeline@u-cergy.fr the Income Inequality Hypothesis and show that income THEMA, University of Cergy-Pontoise, 33 Bd. du Port, 95000 Cergy, France inequalities in a society also matter in order to explain © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adeline and Delattre Health Economics Review (2017) 7:27 Page 2 of 18 the average health status measured by self-perceived mea- by incomes, where income is an individual social determi- sures (mostly in the United States). Concerning the weak nant. This section formally presents the three hypotheses version of the Income Inequality Hypothesis, there are mentioned in the introduction, as well as some related few empirical studies which investigate it, with the excep- literature. We should mention that, in this literature tion of Mellor and Milyo [27] in the United States, Li review, we transcribe terminology employed by authors and Zhu [21] in China or Hildebrand and Van Kerm which reflects causal relationships even if cross-sectional [15] in Europe. Importantly, the strong version of Income databases are used or some endogeneity might be at play. Inequality Hypothesis and the weak version of Income Inequality Hypothesis are non-nested given that the weak The Absolute Income Hypothesis version considers the rank of individuals and an inter- From an early stage in the debate, the Absolute Income action term between the rank and the income inequal- Hypothesis states that the relationship between health and ities index whereas the strong version does not. Thus, income is positive and concave [29], meaning that peo- both versions can be valid when income inequalities in ple with higher incomes have better health outcomes, but a society are negatively associated to the health of all income inequalities have no direct effect on health. As a individuals, and more particularly the health of people result, the concavity of the relationship between individ- ranked at the lower end of the income distribution. How- ual income and health status is a necessary condition to ever, the authors previously mentioned focus mainly on assess the efficiency of redistributive policies, in which one of the versions in the best case (mainly on data transferring a given amount of money from rich people to from the United States), without comparing them. This poor people will result in an improvement of the average paper aims at filling these gaps by looking at the three health. hypotheses, using the same European data, in order to give The individual-level relation between income and more insight about efficient public policies which should health is specified as follows: be implemented in Europe. Finally, studying these three h = β + x β + x β + Z γ +  (1) i 0 i 1 2 i i hypotheses at the same time allows to highlight different where h represents the health status of individual i (objec- mechanisms between health and income. i tive or subjective measures); x is the income of individual In this paper, we test the three above hypotheses with i i; Z is a set of individual specific control variables ;and the Survey of Health, Ageing, and Retirement in Europe i is the error term coming from differences in individual (SHARE), using mainly the fifth wave of this survey i health. The concavity effect is legitimized if β is positive, (2015 release), as well as the pooled version of the sur- 1 ∂h vey in robustness. We use self-perceived health status β is negative, and > 0. ∂x as our health outcome. This type of subjective mea- A strong link between health and income has been sure is sometimes criticized but it is similar to the ones demonstrated in a large number of empirical studies, and used by Mackenbach et al. [26], Fiscella and Franks [13] a concave relationship between the two is found. Preston and Hildebrand and Van Kerm [15]. Furthermore, some [29] explains that the impact of additional income on authors show that these subjective measures are not mortality is greater among the poor than richer people. biased [1]. Lastly, even if this type of measure can be criti- Ettner [12], using three US surveys, finds that increases cized because of interpersonal comparison issues, authors in income improve mental and physical health but also prove that some econometric models tackle these prob- increase alcohol consumption. Then, Mackenbach et al. lems [22] (see “Robustness checks” subsection for some [26] show that a higher income is associated with bet- robustness checks in which we explicitly consider this ter self-assessed health in Europe. Using mortality rates, issue). Cutler et al. [10] conclude the same thing in the United The paper is organized as follows. “Literature review: States. Theodossiou and Zangelidis [31], using data on the relationship between income inequalities and health” individuals aged between 50 and 65 from six European section presents formally the three hypotheses that we will countries, find a positive but small effect of income on test empirically. “Method” section describes the SHARE health. More recently, Carrieri and Jones [8] analyze the datasetaswellasthe baseline econometricspecifica- effect of income on blood-based biomarkers and find a tion. In “Results” section we present the results and some positive and concave effect of income on health. robustness checks. “Conclusion” section concludes the paper. The strong version of Income Inequality Hypothesis Some researchers affirm that income inequalities in a Literature review: the relationship between society are equally important in determining individual income inequalities and health health status. The key difference between the Absolute Inequalities in health refer to the close relationship Income Hypothesis and the strong version of Income between health and membership in a group characterized Inequality Hypothesis stems from the fact that the latter Adeline and Delattre Health Economics Review (2017) 7:27 Page 3 of 18 explicitly considers the effect of income inequalities on from other people in societies where inequalities rise. health while the former only takes into account the con- Thus, reducing social spending turns into a decrease in cavity assumption between health and income. Mellor and life opportunities for poorer people and thus an increase Milyo [27] specifically define two versions of this hypoth- in inequalities (see also [14]). The second mechanism is esis: the strong version and the weak version. The strong that income inequalities lead to the erosion of the “fea- version of the Income Inequality Hypothesis implies that, tures of social organization that facilitate cooperation for whatever the level of income, the health of all individuals mutual benefit”. In other words, Kawachi and Kennedy in a society is equivalently affected by income inequali- [18] interpret this mechanism as the erosion of the “social ties in this society. In this way, both the well-off and poor capital”, corresponding to the set of collective resources people are impacted by income inequalities. These may an individual can put together. This may be the access to be a public bad for all members in a society since income public services, the feeling of security, the characteristics inequalities are a threat to the health of all individuals. of the relatives or the community solidarity (Grignon We can thus identify an individual effect (a micro part) et al.: Mesurer l’impact des déterminants non médicaux which is assimilated to the Absolute Income Hypothesis des inégalités sociales de santé, unpublished). Here we and an aggregate effect (a macro part) which corresponds focus on the solidarity argument. This one is important to the relationship between individual health and income for the maintenance of population health. Kawachi and inequalities in a society. Theoretically, the strong ver- Kennedy [18] made a study using the General Social sion of the Income Inequality Hypothesis is specified as Survey where each indicator of social capital (like the follows: degree of mistrust or levels of perceived reciprocity) was correlated with lower mortality rates. An increasing level h = β + x β + x β + δII + Z γ +  (2) ij 0 i 1 2 j i ij of mistrust between the members of a society was due which is an expansion of Eq. (1) with the introduction of to the development of the distance between the well-off’s II as a measure of income inequalities in a society j (cor- expectation and the ones of poorer people. Unfortunately responding to the macro part explained above); where h this result implies a growth of a latent social conflict. ij represents the health status of individual i in a society j. As a result, when health is associated to the erosion of This hypothesis has been empirically tested mainly on social capital, this seems to be towards the transition data from developed countries (principally in the United of social policies which are detrimental to poor people, States). Tests have been conducted at both the individual implying unequal political participation. A lower turnout level and the aggregate level. At the aggregate level, a at elections is perceived among states with low levels of interpersonal trust. These states are less likely to invest number of studies try to demonstrate an association in policies that ensure thesecurityofpoorerpeopleina between income inequalities and public health and the society. Finally less generous states are likely to provide results are contrasted [17, 25, 30]. At the individual level, less hospitable environments for these individuals. The Kawachi et al. [19], Kennedy et al. [20], and Fiscella and last mechanism is that income inequalities are correlated Franks [13] all find a negative association between income to unhealthiness through stressful social comparisons. inequalities and self-perceived health. However, Van In this case, a technique in anthropology called “cul- Doorslaer et al. [32] find no effect of income inequalities on an objective health measure, the McMaster health tural consensus analysis” is used to take into account utility index, derived from the self-perceived health the psychosocial effects of social comparisons. Indeed, status. Finally, other authors test the impact of income many communities have a common cultural model of the inequalities on malnutrition [33] or health service use standard of living. This technique involves interviewing [23] and find contrasted results. people and observing if individuals succeed in achieving The strong version focuses on the direct ties between the cultural model of lifestyle. This aspect can be seen as health and income inequalities. There are several poten- the satisfaction individuals have with their life. However, tial pathways through which income inequalities might it should be noticed and not forgiven that a possible endo- be negatively related to an individual’s health. Kawachi geneity issue can appear with this mechanism connected and Kennedy [18] summarize three plausible mechanisms to the life satisfaction of individuals. linking income inequalities to health. The first one is that disinvestment in human capital is linked to income The weak version of Income Inequality Hypothesis inequalities. In states with high income inequalities, edu- The second version of the Income Inequality Hypothesis cational outcomes are negatively impacted when a smaller is the weak one. According to this hypothesis, people who proportion of the state budget is spent on education which aremorelikelytohavepoorerhealthare theoneswho creates differences in education and thus in income. High feel more economically disadvantaged than their peers in a reference group. As a result, it specifically suggests that income disparities may translate into lower social spend- only the least well-off are hurt by income inequalities in ing because interests of richer persons begin to diverge Adeline and Delattre Health Economics Review (2017) 7:27 Page 4 of 18 a society. The damaging effect of these inequalities on throughout Europe to a sample of households with at health decreases with a person’s income rank. Indeed, for least one member who is 50 and older. These households an individual, stress and depression leading to illness may are re-interviewed every two years in the panel. SHARE be linked to the fact of having a low relative income when is part of a context of an ageing population. It is the compared to another person [9]. The main concern is thus European Commission which has identified the need for on the difficulties that an individual may face when he is scientific knowledge about ageing people in Europe. In situated at the bottom of the social ladder. Theoretically, fact, people of the European Innovation Partnership on the weak version of the Income Inequality Hypothesis is Active and Health Ageing project estimate that in 2050, specified as follows: one in three Europeans will be over 60 years old and one in ten will be over 85 years old. The SHARE survey was h = β + x β + x β + δII + θR (3) ij 0 i 1 2 j ij then constructed in the different European countries under the leadership of Professor Axel Börsch-Supan. + ηR ∗ II + Z γ + ij j i ij In addition, SHARE is harmonized with the Health and Retirement Study (in the United States - HRS) and the which is an expansion of Eq. (2) where we introduce R ij English Longitudinal Study of Ageing (UK - ELSA). as a person’s rank, and the interaction between inequal- ities and a person’s rank (R ∗ II ) to allow the effects The first wave (2004-2005, 27,014 individuals) and the ij j of income inequalities to vary by the relative income second one (2006-2007, 34,393 individuals) were used level in a society. The interaction term allows us to to collect data on health status, medical consumption, know how income inequalities are related to people with socio-economic status and living conditions. The 2008- lower levels of income, compared to other people. There- 2009 survey (Wave 3 - “SHARELIFE”) was extended to fore, this hypothesis suggests that the breadth of the life stories by collecting information on the history of the difference between rich people and poor ones accounts respondents. The number of participants increased from for the health. When testing this equation, δ underlines 12 countries in wave 1, to 15 (+ Ireland, Israel, Poland the strong version of the Income Inequality Hypothesis andCzech Republic)inwave2,and thethirdwavecon- whereas θ and η specifically refer to the weak version. tains information about 14 countries. The fourth wave Thus, if the three previous coefficients are significant and (2010-2011), is a return to the initial questionnaire of the have the right signs, then both the strong and the weak first two waves. It collects data from 56,675 individuals version are correct, meaning that everybody’s health is in 16 European countries. Finally, the fieldwork of the associated to income inequalities, and in particular people fifth wave of this survey was completed in 2013. The following countries are included in the scientific release who are at the lower end of the income distribution. On of 2015: Austria, Belgium, Switzerland, Czech Republic, the other hand, whether only δ (or θ and η respectively) is Germany, Denmark, Estonia, Spain, France, Israel, Italy, significant implies that only the strong version (resp. the Luxembourg, Netherlands, Sweden, and Slovenia. This weak version) is satisfied. wave contains the responses of 63,626 individuals. We As explained in the introduction, only few researches focus on the fifth wave [3] in order to have a great number focus on this hypothesis. Mellor and Milyo [27] use data of individuals who come from different countries. More- from the Current Population Survey and find no consis- over, in order to test and compare the three hypotheses tent association between income inequalities and individ- linking health and income, one has to use the same set of ual health. On the other hand, Li and Zhu [21], using data from China, find that income inequalities are detrimental observations (e.g. the fifth wave of the SHARE survey). for people who are at the lower end of the income hierar- We do not make our analysis using directly the pooled chy. Finally, Hildebrand and Van Kerm [15] also test the database since all the control explanatory variables are hypothesis that income inequalities may affect only the not available in each waves, which is a limitation of least well-off in a society using the European Community this database. Moreover, we also focus on the pooled Household Panel but find no evidence supporting it. database (waves 1, 2, 4 [4–6] and 5) in order to make our results more robust (the third wave is not considered in Method the pooled database since it does not contain the same The data information as the other ones). The survey The advantage of the SHARE database is that it has The Survey of Health, Ageing and Retirement in Europe many individual variables on health, socioeconomic sta- (SHARE) is a multidisciplinary and cross-national panel tus and income to perform this research. However, database of micro data on health, socio-economic status researchers should be also aware of the potential dis- advantage of this database. Indeed, Börsch-Supan et al. and social and family networks of more than 123,000 [7] explain that in some waves there are a relative low individuals aged 50 and over from many European coun- response rates and moderate levels of attrition (even tries and Israel [7]. Since 2004, SHARE asks questions Adeline and Delattre Health Economics Review (2017) 7:27 Page 5 of 18 though the overall response rate is high compared to other As a result, one of the solution is to use the Theil index European and US surveys ) which are due to the eco- which measures income inequalities. The Theil index is: nomic crisis faced by some countries, implying a decrease 1 y y i i in theparticipation rates. Duetothisattrition,wethus Theil = ln (5) N y ¯ y ¯ focus on the fifth wave of this survey instead of the pooled database. Nonetheless, we present the results using the pooled database as a robustness test. where y ¯ is the mean income per person (or expenditure per capita). In order to normalize the Theil index to vary Indexes for the measurement of income inequalities 6 between zero and one, we divide it by ln(N ). It mea- In this study, we want to underline the effects of income sures a “distance” of the real population and the “ideal” inequalities on health and this is why we need a measure- egalitarian state where everyone have the same income. ment of income inequalities. The Gini coefficient, as well Since the Gini coefficient does not take into account the as the Theil index are two well-known indexes which can income distribution, most of the following tables of results be used. will be displayed using the Theil index. Algebraically, the Gini coefficient is defined as half of the arithmetic average of the absolute differences between Descriptive statistics - an overview all pairs of incomes in a population, and then the total In this paper, the data used are from the fifth wave of the is normalized on mean income. If incomes in a popula- SHARE survey. This wave includes responses from 63,626 tion are distributed completely equally, the Gini value is respondents aged 50 and over, living in 15 different coun- zero, and if one person has all the incomes in a society, the tries. Thus, this survey aims to provide information on Gini is one. The Gini coefficient can be illustrated through health, income, activities and other features of the elderly. the Lorenz curve. However, the Gini coefficient does not In one hand, the variable of interest is the health which is take into account the income distribution since different defined in the database as the self-perceived health status. Lorenz curves may correspond to the same Gini index. In Individuals are asked to classify their health using ordered other words, it does not distinguish between inequalities qualitative labels from “poor” to “excellent. The Fig. 1 in low income group and high income ones. Formally, the characterizes the distribution of the health variable among Gini coefficient is: individuals aged 50 and older by gender for all countries. As we can see the majority of inhabitants reports being in 2 iy N + 1 Gini = − (4) a good health. In the other hand, one of our main deter- N y N minant of health is the income. This variable can be seen with y representing the income of the population sorted as a proxy for well-being, that is to say a factor which and ranked, from the lowest decile group to the top decile allows individuals to improve the living standards. In the group, and N representing the total population. database, it corresponds to the sum of individual imputed Fig. 1 Self-perceived health in Europe Adeline and Delattre Health Economics Review (2017) 7:27 Page 6 of 18 income for all household components. Figure 2 shows the distribution of income of people aged 50 and over in the fifth wave where the mean is about 36,000e.Moreover, the income inequality hypothesis includes an indicator for the measurement of income inequalities (see Fig. 3). In this paper, we use either the Gini index or the Theil index. The mean of the Gini index in Europe is 0.39 which cor- responds to a rather egalitarian society. The mean of the Theil index in Europe is 0.33 which is also rather egali- tarian. In our analysis we include others variables such as the age, the marital status, the education, the job situation, dummies for the countries and the gender, and the GDP of the countries (see Tables 2, 3, 4 and 5 in the Appendix for further information). Finally, the pooled data (waves Fig. 3 Income inequalities indexes in Europe 1, 2, 4 and 5) contains 181,708 observations, where each individual is present on average 2.9 years in the panel. is equalto1,2,3,4or 5for “poor”,“fair”, “good”, “very The ordered probit model good” or “excellent” with this probability: To model the association between self-perceived health and other socioeconomic status and test the hypothe- P(y = j|x) = F(μ − x β) − F(μ − x β) (8) j i j−1 i ses, we use an ordered probit specification. When the The interval decision rule is: self-perceived health status outcome is denoted as h ,the model can be stated as: ∗ 1. h = 1 if h ≤ μ ; i 1 2. h = 2 if μ < h ≤ μ ; i 1 2 h = j iff μ < h ≤ μ,(6) i j−1 j i 3. h = 3 if μ < h ≤ μ ; i 2 3 for j = 1, 2, 3, 4, 5 4. h = 4 if μ < h ≤ μ ; i 3 4 The latent variable specification of the model that we 5. h = 5 if h >μ . i 4 estimate can be written as: In this model, the threshold values (μ , μ , μ , μ )are 1 2 3 4 h = x β +  (7) i i unknown. We do not know the value of the index neces- sary to shift from very good to excellent. In theory, the where h is a latent variable which underlies the self- threshold values are different for everyone. reported health status ; x is a set of observed socioe- conomic variables; and  is an individual-specific error Results term, which is assumed to be normally distributed. Economic results and discussion In this data, the latent outcome h is not observed. Table 1 reports coefficient estimates for all estimated Instead, we observe an indicator of the category in which ordered probit models when income inequalities are mea- the latent indicator falls. As a result the observed variable sured using the Theil index. The fifth wave gives us access to 63,626 observations and we also display results of the pooled database for sake of robustness (see Table 6 in the Appendix section). Results in the first column reports theestimated coefficients forthe absoluteincomehypoth- esis while results in columns two and three provide tests of both the strong version and the weak version of the income inequality hypothesis. Coefficients of individual income and income squared provide support for all the hypotheses that there is a positive and concave relationship between income and self-perceived health status. Indeed, coefficients associ- ated to the income variable are all positive and significant and coefficients associated to the income squared variable are all negative and significant. This implies that higher income is related to a better health outcome. As a result, Fig. 2 Distribution of income in Europe the absolute income hypothesis is verified. Concerning Adeline and Delattre Health Economics Review (2017) 7:27 Page 7 of 18 Table 1 Results of the ordered probit regressions for Wave 5 Variables Absolute income IIH Hypothesis Strong version Weak version ∗∗∗ ∗∗∗ ∗∗∗ Income 1.84e-06 1.84e-06 1.89e-06 (1.22e-07) (1.20e-07) (1.44e-07) ∗∗∗ ∗∗∗ ∗∗∗ Income squared −2.06e-13 −2.04e-13 −2.09e-13 (1.55e-14) (1.50e-14) (1.73e-14) Quintiles of income: Reference - Q5 ∗∗∗ Quintile 1 −0.258 (0.029) ∗∗∗ Quintile 2 −0.201 (0.028) ∗∗∗ Quintile 3 −0.115 (0.027) ∗∗∗ Quintile 4 −0.053 (0.026) ∗∗∗ ∗∗∗ Index of inequalities (II) - Theil −0.403 −0.838 (0.024) (0.049) Interaction quintile 1 and II 0.115 (0.069) Interaction quintile 2 and II 0.114 (0.068) Interaction quintile 3 and II 0.023 (0.068) Interaction quintile 4 and II 0.062 (0.068) ∗∗∗ ∗∗∗ GDP 1.99e-06 0.0001 (4.53e-07) (0.049) ∗∗∗ ∗∗∗ ∗∗∗ Age 0.037 0.019 0.037 (0.006) (0.006) (0.006) ∗∗∗ ∗∗∗ ∗∗∗ Age squared −0.0004 −0.0003 −0.0004 (0.00004) (0.0004) (0.00004) ∗∗∗ ∗∗∗ ∗∗∗ Years of education 0.034 0.028 0.026 (0.001) (0.001) (0.001) Gender = 1 if women 0.003 0.005 0.007 (0.009) (0.009) (0.009) Marital Status: Reference - Married Registered partnership −0.042 −0.006 0.058 (0.035) (0.035) (0.035) ∗∗ ∗∗ Married, not living with spouse −0.094 0.004 −0.076 (0.039) (0.039) (0.039) ∗∗∗ Never married −0.071 0.023 0.023 (0.019) (0.019) (0.019) ∗∗∗ ∗∗∗ ∗∗ Divorced −0.045 0.068 0.032 (0.016) (0.018) (0.015) ∗ ∗∗∗ Widowed −0.024 0.055 0.015 (0.015) (0.014) (0.014) Job Situation: Reference Retired ∗∗∗ ∗∗∗ ∗∗∗ Employed 0.253 0.224 0.246 (0.014) (0.014) (0.014) ∗∗∗ ∗∗∗ ∗∗∗ Unemployed −0.212 −0.103 −0.176 (0.028) (0.028) (0.028) ∗∗∗ ∗∗∗ ∗∗∗ Permanently sick −1.25 −1.069 −1.207 (0.026) (0.026) (0.026) ∗∗∗ ∗∗∗ ∗∗∗ Home-maker −0.059 −0.064 −0.056 (0.017) (0.017) (0.017) ∗∗∗ ∗∗∗ ∗∗∗ Other −0.236 −1.169 −0.207 (0.031) (0.031) (0.031) Mechanisms IIHs: st ∗∗∗ 1 : % Health expenditure in GDP 0.077 (0.003) nd ∗∗∗ 2 : Received help from others −0.179 (0.006) nd ∗∗∗ 2 bis: Given help from others 0.001 (0.0001) rd ∗∗∗ 3 : Life satisfaction 0.216 (0.003) Cut-point μ −0.474 0.899 −0.428 (0.216) (0.219) (0.215) Adeline and Delattre Health Economics Review (2017) 7:27 Page 8 of 18 Table 1 Results of the ordered probit regressions for Wave 5 (Continued) Cut-point μ 0.615 2.076 0.632 (0.216) (0.219) (0.215) Cut-point μ 1.746 3.261 1.728 (0.216) (0.219) (0.215) Cut-point μ 2.592 4.133 2.548 (0.216) (0.219) (0.215) ME at mean of absolute income on: ∗∗∗ ∗∗∗ ∗∗∗ Pr(Poor health) −2.84e-07 −2.58e-07 −3.02e-07 (1.92e-08) (1.71e-08) (2.32e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Fair health) −3.06e-07 −2.97e-07 −3.24e-07 (2.05e-08) (1.95e-08) (2.49e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Good health) 8.80e-08 6.65e-08 9.56e-08 (6.44e-09) (4.97e-09) (7.80e-09) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Very good health) 2.65e-07 2.55e-07 2.79e-07 (1.78e-08) (1.68e-08) (2.14e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Excellent health) 2.37e-07 2.34e-07 2.51e-07 (1.59e-08) (1.54e-08) (1.92e-08) For AIH, dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant. Standard deviations are in parentheses, below the coefficients. income inequalities, coefficients on the Theil index in inequality hypothesis. Concerning the two other interac- columns two and three are negative and significantly dif- tion terms (third and fourth quintiles, representing people ferent from zero. This supports evidence of the strong at the middle and almost top of the income distribution), version of income inequality hypothesis stating that an coefficients are not statistically significant meaning that increase in income inequalities is detrimental to all mem- middle and higher income people are not affected at all bers of a society, i.e. income inequalities and health are by an increase in income inequalities. This claim does not negatively related. Indeed concerning this index, zero rep- support the weak version because this hypothesis states resents an egalitarian state, thus the negative relationship that people at the lower end are the most affected by an between self-perceived health and the indicator of income increase in income inequalities compared to people at the inequalities is in line with health being better if the index is top of the income distribution. As a result, higher income low. However, results in column three do not give support people should also be affected by income inequalities (at to the weak version of income inequality hypothesis which a lower rate). Our qualitative results suggest that for low- states that inequalities are more detrimental to the least income individuals, an increase in income inequalities well-off in a society. Indeed, we introduce individual rank in their country is positively related to report a better (by country) and an interaction term between the rank health status. Furthermore, for higher income individuals, and the index of income inequalities to allow a variation an increase in income inequalities in their country is not between income level and the effect of income inequali- related to report neitherabetter noralowerhealthstatus. ties. In the specification, we choose to follow the frame- To conclude, our results do not support the weak version work of Mellor and Milyo [27] who introduced interaction of income inequality hypothesis, but it further invalidates terms between the measurement of income inequalities this weak version because our qualitative results quite and dummies variables based on quintiles of income (1 for claim the opposite. the lowest income group and 5 for the highest, which is Regarding the mechanisms of Kawachi and Kennedy a proxy for the rank). In other words, interaction terms [18] (Table 1, column two), the disinvestment in human indicate the effect of aggregate income inequalities (at capital(firstmechanism)ischaracterized by theper- the country level) on self-perceived health status between centage of health expenditure in the GDP. The coeffi- individuals situated at different levels of the income distri- cient associated is positively correlated to health meaning bution. Concerning the first two interaction terms (II ∗Q1 that when governments increase health spending, this and II ∗ Q2), these indicate the effect of aggregate income has a positive effect on individual health. For the sec- inequalities (at the country level) on self-perceived health ond mechanism, we want to illustrate the interaction status between the poorest individuals (situated at the between individuals to represent the erosion of social cap- lower end of the income distribution) and the richest ital. As a result, we choose a variable from the SHARE ones (reference category corresponding to individuals sit- survey: “received help from others”. The coefficient asso- uated at the top of the income distribution). These coef- ciated to this variable is negative and significant. We ficients are positive and statistically significant, meaning can explain this negative association by saying that peo- that for the poorest individuals (compared to more well- ple who are in bad health are the ones who receive off individuals), an increase in income inequalities in their help. In order, to legitimize this explanation, we also country increases self-perceived health status, which is do the estimation with the “reverse variable”: “given in contradiction with the weak version of the income help to others”. In this case, the coefficient is positive Adeline and Delattre Health Economics Review (2017) 7:27 Page 9 of 18 and significant proving that people in good health offer the impact of income on the probability to report a good their help. Then, the last mechanism is about social health status. For almost all the distribution, when income comparisons. The coefficient associated to this variable raises, the probability increases. Then, graphs 4d and 4e (“life satisfaction”) is positively linked to health which are more conclusive. Indeed, graph 4d gives the impact implies that when individuals are satisfied with their life, of income on the probability to have a very good health. they also report having a good health. For more than 99% of the income distribution, this impact In sum, our baseline specifications provide evidence is positive and decreasing, which might support the con- of a statistically significant association between income, cavity assumption. Finally, graph 4e gives the impact of income inequalities and health since results are robust to income on the probability of reporting an excellent health model specifications. status. As previously, when income increases, the proba- bility to have an excellent health increases. However, when Robustness checks we look at people with very high incomes ,thisimpactis As a sake of robustness, we also make our entire analysis greater than for the majority of individuals. using the pooled database (see Table 6 in the Appendix Finally, it is important to investigate the robustness of section) and the results are very similar to the ones our results by taking into account the subjective nature obtained with the fifth wave of the survey. of the self-perceived health status. Indeed, our baseline To give more support to the concavity assumption, we specification depends on a dependent variable which is compute, for all three hypotheses, the marginal effects at subjective. Self-reported measures give a good amount mean of income on the five outcomes. Results, reported of information about individual health since people sum- at the end of Table 1, are all significant. On one hand, marize all the health information they have from their for the first two outcomes, income has a negative effect practitioners (general practitioners and specialists) and on the probability to report either a poor health or a fair from what they feel [1]. The use of this measure in our health status. On the other hand, there is a positive effect specification raises the problem of interpersonal com- of income on the probability to report being in a good, parisons between people aged 50 and over (“Is the way very good and excellent health (outcomes three to five). I consider “good health” the same as you consider this These results are obtained following the ordered probit health commodity?”. Empirical studies on the relationship regressions of the three hypotheses, where the quadratic between health, income and income inequalities com- effect of income is investigated (see Eqs. 1, 2 and 3). These monly use ordered probit models where the thresholds results do not validate the concavity assumption but they are constant by assumption. However, one limit is that it restricts the marginal probability effects. In fact the dis- do show the increasing effect of income on self-perceived health status. We also plot the average marginal effect tributional effects are restricted by the specific structure. of income on each outcome for all individuals with a Then, another limit is that additional individual hetero- confidence interval, in order to give more support to geneity between individual realizations is not allowed by the concavity effect in the three hypotheses (see Fig. 4). the distributional assumption. Thus, Boes and Winkel- We restrict ourselves to individuals who earn less than mann [2] and Jones and Schurer [16] both give a solution 200,000e per year (which corresponds to more than 99% to these issues with the use of the generalized ordered pro- of the distribution, see Table 4 in the Appendix section bit model since it is based on a latent threshold where the for further information on the distribution of income). thresholds themselves are linear function of the explana- The following graphs (Fig. 4) concern the absolute income tory variables. In other words, previous thresholds of Eq. 8 hypothesis. Graph4agives theimpactofincomeonthe are now computed by selecting individual characteristics probability to report a poor health. This impact is negative so that they depend on covariates: (y-axis is negative), meaning that when income raises, the μ =  μ + x γ (9) ij j j probability decreases. In addition, the negative impact is stronger for the majority of the population than for indi- where γ is a vector of response specific parameters. We viduals who earn very high incomes. In other words, for have: low incomes, in absolute terms, an additional increase in income has a larger impact on the probability of report- μ = μ ∀ ∈ C (10) ij j i j ing a poor health than for very high income. This is a low where C is the class. With this model, the probabilities support for the concavity assumption. Graph 4b gives the are: impact of income on the probability of reporting a fair health status. Conclusion are similar to the ones of graph P(y = j|x) = F( μ − x β ) − F( μ − x β ) (11) j i j j−1 i j−1 4a since the effect is negative. The slight decreases of the curve at the beginning does not impact the conclusion and Now, the effects of covariates on the log-odds are can be related to large confidence intervals. Graph 4c gives category-specific and this model allows to have more Adeline and Delattre Health Economics Review (2017) 7:27 Page 10 of 18 Fig. 4 Average marginal effects of income on health - Absolute Income Hypothesis. a Probability to report a poor health; b Probability to report a fair health; c Probability to report a good health; d Probability to report a very good health; e Probability to report an excellent health heterogeneity across individuals. Results concerning the inequalities is negative and significant which is in line generalized ordered probit model are similar to those with the strong version of the income inequality hypoth- obtained from the ordered probit model. All the effects esis. Then, concerning the interaction terms, these are are estimated around each four cut-points (from poor to not significant for all quintile groups which do not justify fair, from fair to good, from good to very good, and from the weak version of income inequality hypothesis. Finally, very good to excellent). For all the hypotheses (absolute adding some heterogeneity in this model and taking into income hypothesis - Appendix: Table 7, income inequal- account the issues of interpersonal comparisons do not ity hypothesis, both versions - Tables 8 and 9 in the modify our previous results. Appendix part), the coefficients associated to the vari- ables of interest (income and income squared) do not Conclusion change significantly in comparison to the results with the In this study we underline the hypotheses through which ordered probit model. Results are consistent (either with health is associated to income and income inequalities. the Theil index or the Gini coefficient for the income The aim of this paper is to empirically investigate the evi- inequality hypothesis) as this is proved in previous study dence for the absolute income hypothesis and both the [22]. In fact, in the four cut-points, the results legitimize strong and the weak versions of the income inequality the concavity assumption of income since the coefficients hypothesis for people aged 50 and over in Europe, using are statistically significant. Moreover, the index of income data from theSHARE survey.Indeed,wereviewthe Adeline and Delattre Health Economics Review (2017) 7:27 Page 11 of 18 relationship on income-related health inequalities where good effects through individual levels of health. There will we mention the literature as well as the theoretical and be like a virtuous circle in which incomes influence the statistical tools needed to carry out this research. Then health status (improving the production possibilities of we present the data used and some descriptive statistics. the economy can be achieved by improving the health) Finally we show the model specification, the results of the which in turn affects the income. three hypotheses and some robustness tests. This whole work, both the literature study and the establishment of Endnotes various models led us to estimate different assumptions on In this way, redistributing income from rich people to the relationship between health and income. This study is poor people will have an important and positive impact on one of the first analyzing this relationship through differ- the health of the poorer people, whereas the richer ones ent hypotheses at the same time using the SHARE survey which is a rich database, containing a lot of information will experience a small decrease in their health. on elderly people and countries simultaneously. Such as age, gender, number of years of education, We find evidence supporting the absolute income marital status and the job situation. It can also contain hypothesis which states that people with higher incomes countries dummies variables. have better health outcomes. We also find evidence See http://ec.europa.eu/ for an explanation of the supporting the strong version of income inequality European Innovation Partnership on Active and Healthy hypothesis which argues that inequality affects all mem- bers in a society equivalently. In this hypothesis, we find Ageing - A Europe 2020 initiative. that when there are high income inequalities in a country, After wave four was completed, the average retention people aged 50 and over feel less healthy. However, we do rate over the year was 81%. not find evidence supporting the weak version of income For instance, if 50 percent of the population has no inequality hypothesis which states that only the least income and the other half has the same income, the Gini well-off are hurt by income inequalities in a society. This index is 0.5. The same result can be found with the fol- hypothesis underlines the fact that income inequalities are more detrimental for the health of people with low lowing analysis which is less unequal. On one hand, 25 incomes. Our qualitative results suggest that for low- percent of total income is shared in the same way by income individuals, an increase in income inequalities in 75 percent of the population, and on he other hand, the their country is positively related to report a better health remaining 25 percent of the total income is divided by the status. Furthermore, for higher income individuals, an remaining 25 percent of the population. increase in income inequalities in their country is not It is this normalized index that we use hereafter and related to report either a better or a lower health status. One limitation is the used of cross-sectional data with- that we name the Theil index. 7 ∗ out investigating possible endogeneity issues. Thus our Once h crosses a certain value you report fair, then results highlight statistical associations rather than causal poor, then good, then very good, then excellent health. effects. Finally, by implementing the generalized ordered Results associated to the Gini coefficient are not pro- probit, we control for potential problems of interpersonal vided here but they are very similar and available upon comparisons and the results are very similar to those request. found with the ordered probit model. Results concerning the hypotheses are consistent with Source: OECD website. the concavity assumption of income on health. Extension We look at the average individual of the database and would be to highlight causal effects, using other meth- compute the marginal effects. ods, in order to support some political implication. In fact, We do not include the ones for the income inequality what is important in determining the health status is more hypothesis (both versions) since the results are very simi- how income is distributed in a society and less the overall lar and do not change the main conclusion, but these are health of this society. As a result, the more equally income is distributed, the better the overall health in this soci- available upon request. ety. Concerning political implication, one way to improve In this case, people with very high incomes are indi- health might be to take measures using the redistribution viduals who earn more than 150,000e per year, corre- of incomes as a lever. In fact, Lynch et al. [24] argue that, sponding to less than 2% of the sample. redistributive fiscal and tax policies will help the govern- ments to achieve better population health. Deaton [11] explains that if income inequalities affect health, transfer Appendix policies that affect the distribution of incomes would have Descriptive Statistics Adeline and Delattre Health Economics Review (2017) 7:27 Page 12 of 18 Table 2 Descriptive statistics of the variables Variables Mean Standard deviation Minimum Maximum Health Self-perceived health status (N = 63626) 2.85 1.09 1 5 Inequalities Gini per country 0.39 0.05 0.31 0.48 Theil per country 0.33 0.19 0.16 0.82 Other Variables Income 36,621.21 71,863.78 2 1.00e+07 GDP per country (2013 - Dollar US/capita) 39,726.43 11,543.57 26,160.08 92,781.41 Education 11.12 4.28 1 25 Age 67.12 10.06 50 103 Table 3 Detailed descriptive statistics for the health Health Percentage of people Poor (1) 10.81% Fair (2) 27.01% Good (3) 36.52% Very Good (4) 17.58% Excellent (5) 8.18% Table 4 Detailed descriptive statistics for income Distribution Income 5% 3,828.99 25% 12,446 50% 24,659.55 75% 46,200 95% 103,897.2 Table 5 Detailed descriptive statistics for the countries Country Percentage of people* GDP - 2013** Indexes of inequality*** Theil index Gini index Austria 6.54% 45 132.54 0.1762 0.3222 Germany 8.71% 43 282.31 0.2234 0.3672 Sweden 7.06% 44 585.87 0.1672 0.3183 Netherlands 6.42% 46 749.31 0.2152 0.3543 Spain 9.75% 33 111.45 0.2521 0.3813 Italy 6.88% 34 836.43 0.373 0.4239 France 6.86% 37 617.06 0.8224 0.4772 Denmark 6.37% 43797.23 0.1578 0.3138 Switzerland 4.62% 56 896.91 0.2144 0.3554 Belgium 8.66% 41 863.94 0.3849 0.4545 Czech Republic 8.7% 28 962.64 0.2123 0.3512 Luxembourg 2.5% 92 781.4 0.2649 0.3979 Israel 3.56% 32 504.72 0.2475 0.3906 Slovenia 4.51% 28 675.43 0.3696 0.451 Estonia 8.88% 26 160.08 0.6816 0.4497 *: From each country in the full sample **: Gross Domestic Product, Total dollar US/capita ***: Values Adeline and Delattre Health Economics Review (2017) 7:27 Page 13 of 18 Additional Econometric Results Table 6 Results of the ordered probit regressions for the pooled database Variables Absolute Income IIH Hypothesis Strong Version Weak Version ∗∗∗ ∗∗∗ ∗∗∗ Income 1.41e-06 1.94e-06 1.16e-06 (4.74e-08) (4.34e-08) (4.76e-08) ∗∗∗ ∗∗∗ ∗∗∗ Income squared −1.78e-13 −2.39e-13 −1.46e-13 (1.14e-14) (1.13e-14) (1.12e-14) Quintiles of income: Reference - Q5 ∗∗∗ Quintile 1 −0.379 (0.019) ∗∗∗ Quintile 2 −0.288 (0.019) ∗∗∗ Quintile 3 −0.184 (0.019) ∗∗∗ Quintile 4 −0.115 (0.018) ∗∗∗ ∗∗∗ Index of inequalities (II) - Theil −0.473 −0.567 (0.018) (0.038) Interaction quintile 1 and II 0.121 (0.053) Interaction quintile 2 and II 0.054 (0.053) Interaction quintile 3 and II −0.012 (0.052) Interaction quintile 4 and II 0.053 (0.052) Interaction quintile 5 and II Reference ∗∗∗ ∗∗∗ GDP 0.0002 0.0002 (3.03e-07) (3.06e-07) ∗∗∗ ∗∗∗ ∗∗∗ Age −0.014 −0.018 −0.015 (0.003) (0.003) (0.003) ∗∗∗ ∗∗ ∗∗∗ Age squared −0.0001 −0.0001 −0.0006 (0.00002) (0.0002) (0.00002) ∗∗∗ ∗∗∗ ∗∗∗ Years of education 0.021 0.019 0.017 (0.001) (0.0005) (0.001) ∗∗∗ ∗∗∗ ∗∗∗ Gender = 1ifwomen −0.055 −0.057 −0.050 (0.005) (0.005) (0.005) Marital Status: Reference - Married ∗∗∗ ∗ Registered partnership −0.060 −0.030 −0.026 (0.017) (0.017) (0.017) ∗∗∗ ∗∗∗ ∗∗∗ Married, not living with spouse −0.098 −0.087 −0.091 (0.009) (0.009) (0.009) ∗∗∗ ∗∗∗ ∗∗ Never married −0.127 −0.108 −0.027 (0.014) (0.013) (0.014) ∗∗∗ ∗∗∗ Divorced −0.079 −0.062 0.016 (0.011) (0.011) (0.011) ∗∗∗ ∗∗∗ ∗∗∗ Widowed −0.046 −0.055 0.026 (0.009) (0.009) (0.009) Waves: Reference - Wave 5 ∗∗∗ ∗∗∗ ∗∗∗ Wave 1 0.139 0.431 0.469 (0.009) (0.009) (0.009) ∗∗∗ ∗∗∗ ∗∗∗ Wave 2 0.094 0.247 0.272 (0.009) (0.009) (0.009) ∗∗∗ Wave 4 −0.024 −0.001 0.003 (0.006) (0.006) (0.006) Cut-point μ −2.494 −1.960 −1.976 (0.104) (0.104) (0.105) Cut-point μ −1.46 −0.952 −0.962 (0.104) (0.105) (0.105) Cut-point μ −0.378 0.106 0.102 (0.104) (0.105) (0.104) Cut-point μ 0.455 0.919 0.919 (0.104) (0.104) (0.105) For AIH, dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant. Standard deviations are in parentheses, below the coefficients Adeline and Delattre Health Economics Review (2017) 7:27 Page 14 of 18 Table 7 Absolute Income Hypothesis - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.99e-06 *** 2.25e-06 *** 3.68e-06 *** 3.81e-06 *** (2.76e-07) (2.00e-07) (2.44e-07) (4.44e-07) Income squared -2.11e-13 *** -7.96e-13 *** -3.26e-13 *** -5.41e-12 *** (2.90e-14) (1.17e-13) (4.71e-13) (1.55e-12) Age 0.037 *** 0.037 *** 0.026 *** 0.029 *** (0.01) (0.008) (0.009) (0.012) Age squared -0.0004 *** -0.0004 *** -0.0004 *** -0.0003 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.031 *** 0.038 *** 0.036 *** 0.024 *** (0.002) (0.001) (0.001) (0.002) Gender = 1 if women 0.066 *** -0.014 -0.005 -0.002 ** (0.016) (0.012) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership -0.063 -0.093 ** 0.029 -0.027 (0.069) (0.046) (0.045) (0.057) Married, not living with spouse -0.251 *** -0.112 ** -0.0001 0.118 * (0.062) (0.049) (0.053) (0.069) Never married -0.048 -0.068 *** -0.038 -0.065 * (0.032) (0.024) (0.026) (0.035) Divorced -0.157 *** -0.059 *** 0.05 *** 0.06 ** (0.026) (0.019) (0.021) (0.027) Widowed -0.017 -0.026 0.002 -0.015 (0.021) (0.017) (0.02) (0.029) Job Situation: Retired Reference group Employed 0.398 *** 0.312 *** 0.203 *** 0.174 *** (0.029) (0.019) (0.019) (0.025) Unemployed -0.222 *** -0.191 *** -0.233 *** -0.126 ** (0.047) (0.035) (0.038) (0.053) Permanently sick -1.196 *** -1.268 *** -1.307 *** -0.963 *** (0.033) (0.038) (0.054) (0.076) Home-maker -0.088 *** -0.052 ** -0.047 * -0.006 (0.029) (0.022) (0.025) (0.035) Other -0.354 *** -0.173 *** -0.145 *** -0.017 (0.041) (0.037) (0.046) (0.064) Dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant 1 to 2: Poor to Fair; 2 to 3: Fair to Good; 3 to 4: Good to VG; 4 to 5: VG to Excellent Adeline and Delattre Health Economics Review (2017) 7:27 Page 15 of 18 Table 8 IIH, strong version - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.75e-06 *** 2.34e-06 *** 3.89e-06 *** 3.20e-06 *** (2.69e-07) (1.97e-07) (2.38e-07) (4.42e-07) Income squared -1.89e-13 *** -8.28e-13 *** -3.75e-12 *** -5.18e-12 *** (2.82e-14) (1.18e-13) (4.72e-13) (1.60e-12) Index of inequalities (Theil) -0.095 ** -0.369 *** -0.7389 *** -0.4746 *** (0.041) (0.031) (0.035) (0.048) Mechanisms: 1st: % Health exp. in the GDP 0.059 *** 0.087 *** 0.073 *** 0.082 *** (0.005) (0.004) (0.004) (0.006) 2nd: Received help from others -0.214 *** -0.193 *** -0.134 *** -0.089 *** (0.009) (0.008) (0.009) (0.013) 2nb bis: Given help to others 0.001 *** 0.001 *** 0.001 *** 0.001 *** (0.0001) (0.0001) (0.0001) (0.0001) 3rd: Life satisfaction 0.195 *** 0.215 *** 0.239 *** 0.238 *** (0.004) (0.003) (0.004) (0.006) GDP 2.52e-06 *** 1.41e-06 ** -4.87e-07 5.94e-07 (8.66e-07) (6.04e-07) (6.36e-07) (8.72e-07) Age 0.019 * 0.004 0.013 0.019 * (0.01) (0.008) (0.009) (0.012) Age squared -0.0003 *** -0.0002 *** -0.0003 *** -0.0003 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.025 *** 0.029 *** 0.028 *** 0.021 *** (0.002) (0.001) (0.0014) (0.0018) Gender = 1 if women 0.069 *** -0.018 -0.003 -0.0004 (0.016) (0.012) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership -0.023 -0.053 0.034 0.014 (0.071) (0.047) (0.045) (0.058) Married, not living with spouse -0.131 ** 0.005 0.091 * 0.122 * (0.065) (0.051) (0.054) (0.072) Never married 0.033 0.023 0.064 ** 0.001 (0.034) (0.025) (0.027) (0.036) Divorced -0.046 * 0.062 *** 0.166 *** 0.122 *** (0.028) (0.021) (0.022) (0.028) Widowed 0.053 ** 0.069 *** 0.076 *** 0.022 (0.023) (0.018) (0.022) (0.031) Job Situation: Retired Reference group Employed 0.344 *** 0.225 *** 0.177 *** 0.176 *** (0.03) (0.019) (0.019) (0.025) Unemployed -0.141 *** -0.097 *** -0.11 *** 0.012 (0.048) (0.035) (0.039) (0.054) Adeline and Delattre Health Economics Review (2017) 7:27 Page 16 of 18 Table 8 IIH, strong version - Generalized ordered probit (Wave 5) (Continued) Permanently sick -1.016 *** -1.121 *** -1.098 *** -0.744 *** (0.034) (0.034) (0.056) (0.084) Home-maker -0.074 *** -0.033 -0.076 *** -0.044 (0.029) (0.022) (0.025) (0.035) Other -0.299 *** -0.114 *** -0.09 * 0.048 (0.043) (0.038) (0.048) (0.067) ***: 1% significant; **: 5% significant; *: 10% significant 1 to 2: Poor to Fair; 2 to 3: Fair to Good; 3 to 4: Good to VG; 4 to 5: VG to Excellent Table 9 IIH, weak version - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.97e-06 *** 3.03e-06 *** 5.92e-06 *** 7.65e-06 *** (3.06e-07) (2.43e-07) (3.15e-07) (6.10e-07) Income squared -2.09e-13 *** -1.14e-12 *** -6.03e-12 *** -1.60e-11 *** (3.17e-14) (1.25e-13) (5.21e-13) (1.92e-12) Index of inequalities (Theil) -0.319 *** -0.79 *** -1.077 *** -0.899 *** (0.101) (0.065) (0.065) (0.084) Quintile 1 -0.145 *** -0.195 *** -0.003 0.07 (0.055) (0.039) (0.043) (0.059) Quintile 2 -0.099 * -0.159 *** -0.014 0.079 (0.054) (0.038) (0.039) (0.059) Quintile 3 -0.061 -0.043 0.018 0.025 (0.054) (0.037) (0.037) (0.047) Quintile 4 -0.012 -0.02 0.055 0.023 (0.056) (0.036) (0.034) (0.043) Quintile 5 Reference group Interaction quintile 1 and II -0.204 * 0.079 -0.039 0.084 (0.12) (0.088) (0.107) (0.147) Interaction quintile 2 and II -0.162 0.097 0.048 0.029 (0.123) (0.087) (0.101) (0.138) Interaction quintile 3 and II -0.163 -0.048 -0.013 0.144 (0.125) (0.088) (0.098) (0.129) Interaction quintile 4 and II -0.058 0.066 0.001 0.098 (0.132) (0.088) (0.093) (0.124) Interaction quintile 5 and II Reference group GDP 0.0001 *** 9.96e-06 *** 3.83e-06 *** 2.17e-06 *** (8.30e-07) (6.31e-07) (6.99e-07) (9.91e-07) Age 0.034 *** 0.023 *** 0.029 *** 0.034 ** (0.01) (0.008) (0.008) (0.011) Age squared -0.0004 *** -0.0003 *** -0.0004 *** -0.0004 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.025 *** 0.029 *** 0.028 *** 0.022 *** (0.002) (0.001) (0.001) (0.002) Adeline and Delattre Health Economics Review (2017) 7:27 Page 17 of 18 Table 9 IIH, weak version - Generalized ordered probit (Wave 5) (Continued) Gender = 1 if women 0.066 *** -0.016 0.0004 0.007 (0.015) (0.011) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership 0.053 0.023 0.075 * 0.049 (0.067) (0.045) (0.044) (0.056) Married, not living with spouse -0.203 *** -0.091 * -0.014 0.052 (0.061) (0.049) (0.052) (0.068) Never married 0.034 0.014 0.042 -0.008 (0.033) (0.024) (0.026) (0.035) Divorced -0.079 *** 0.009 0.107 *** 0.085 *** (0.027) (0.02) (0.021) (0.027) Widowed 0.024 0.015 0.019 -0.015 (0.022) (0.018) (0.021) (0.029) Job Situation: Retired Reference group Employed 0.374 *** 0.251 *** 0.206 *** 0.188 *** (0.029) (0.019) (0.018) (0.024) Unemployed -0.188 *** -0.169 *** -0.221 *** -0.128 ** (0.046) (0.034) (0.038) (0.053) Permanently sick -1.162 *** -1.262 *** -1.245 *** -0.923 *** (0.032) (0.033) (0.054) (0.08) Home-maker -0.062 ** -0.021 -0.081 *** -0.069 ** (0.027) (0.021) (0.024) (0.034) Other -0.317 *** -0.152 *** -0.148 *** -0.017 (0.041) (0.037) (0.046) (0.064) ***: 1% significant; **: 5% significant; *: 10% significant 1 to 2: Poor to Fair; 2 to 3: Fair to Good; 3 to 4: Good to VG; 4 to 5: VG to Excellent Adeline and Delattre Health Economics Review (2017) 7:27 Page 18 of 18 Acknowledgements 15. 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Brit Med J. 1996;312(7037):999–1003. of Education and Research, the U.S. National Institute on Ageing (U01 18. Kawachi I, Kennedy BP. Income inequality and health: pathways and AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, mechanisms. Health Serv Res. 1999;34(1):215. Y1-AG-4553-01, IAG BSR06-11, OGHA 04-064) and from various national 19. Kawachi I, Kennedy BP, Lochner K. Prothrow-Stith. Social capital, income funding sources is gratefully acknowledged (see www.share-project.org).” inequality, and mortality. Am J Public Health. 1997;87(9):1491–98. We thank participants of the 5th SHARE User’s Conference, the EuHEA 20. Kennedy BP, Kawachi I, Glass R, Prothrow-Stith D, et al. Income conference and the third EuHEA PhD student and supervisor conference, distribution, socioeconomic status, and self rated health in the United Fabian Gouret and Toni Mora for their helpful comments, as well as Richard States: a multilevel analysis. Brit Med J. 1998;317(7163):917–21. 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The relationship between health and schooling: What’s new? Tech. rep. National Bureau of Economic Research. 2015. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Health Economics Review Springer Journals

Some microeconometric evidence on the relationship between health and income

Adeline, Amélie; Delattre, Eric
Health Economics Review , Volume 7 (1) – Aug 14, 2017
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Springer Journals
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Copyright © 2017 by The Author(s)
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Medicine & Public Health; Public Health; Health Economics; Public Finance; Pharmacoeconomics and Health Outcomes; Economic Policy; Health Care Management
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10.1186/s13561-017-0163-5
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Abstract

This paper examines the association between income, income inequalities and health inequalities in Europe. The contribution of this paper is to study different hypotheses linking self-perceived health status and income, allowing for the identification of different mechanisms in income-related health inequalities. Using data from the Survey of Health, Ageing and Retirement in Europe (15 countries), we take the advantage of the cross-sectional and longitudinal nature of this rich database to make robust results. The analyses (coefficient estimates as well as average marginal effects) strongly support two hypotheses by showing that (i) income has a positive and concave effect on health (Absolute Income Hypothesis); (ii) income inequalities in a country affect all members in a society (strong version of the Income Inequality Hypothesis). However, our study suggests that, when considering the position of the individual in the income distribution, as well as the interaction between income inequalities and these rankings, one cannot identify individuals the most affected by income inequalities (which should be the least well-off in a society according to the weak version of the Income Inequality Hypothesis). Finally, the robustness of this study is emphasized when implementing a generalized ordered probit to consider the subjective nature of the self-perceived health status to avoid the traps encountered in previous studies. JEL Classification: IOO, I14, D31 Keywords: Health inequalities, Income inequalities, Self-reported health, Europe Background relationship between income and health. Higher incomes The last few years have seen unprecedented attention to can provide means for purchasing a better health sta- an attempt by policy makers, policy advisers and interna- tus. The second one is the strong version of the Income tional institutions to reduce health inequalities. To do so, Inequality Hypothesis and it asserts that the health sta- they usually focus on the access to healthcare, given that tus is determined by income inequalities within a society. such policies allow to improve the health of lower income Thus, the health of all individuals is affected by an increase groups [28, 34]. Improving equality of access to health- or a decrease in income inequalities. The last one, a care is however not the sole public policy which can favor weak version of the Income Inequality Hypothesis, says health equality. In particular, it has been widely said that that income inequalities are a threat to individuals placed income and income inequalities are associated to health at the lower end of the income distribution. This last status; thus, any public policy which influences income hypothesis implies that income inequalities do not impact and/or income inequalities might influence health. In this low income people and high income people in the same way, studying the relationship between income, income magnitude. inequalities and health is interesting per se. With these Various authors have studied the Absolute Income elements in mind, this paper confronts on an empir- Hypothesis mainly in the United States, using different ical basis three hypotheses. The first one, called the health measures, like self-perceived measures [26], life Absolute Income Hypothesis, was initially introduced by expectancy [10] and other health outcomes [8, 12]. Fiscella Preston [29] and states that there is a positive and concave and Franks [13], Kennedy et al. [20], Van Doorslaer et al. [32], Wagstaff et al. [33] focus on the strong version of *Correspondence: amelie.adeline@u-cergy.fr the Income Inequality Hypothesis and show that income THEMA, University of Cergy-Pontoise, 33 Bd. du Port, 95000 Cergy, France inequalities in a society also matter in order to explain © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adeline and Delattre Health Economics Review (2017) 7:27 Page 2 of 18 the average health status measured by self-perceived mea- by incomes, where income is an individual social determi- sures (mostly in the United States). Concerning the weak nant. This section formally presents the three hypotheses version of the Income Inequality Hypothesis, there are mentioned in the introduction, as well as some related few empirical studies which investigate it, with the excep- literature. We should mention that, in this literature tion of Mellor and Milyo [27] in the United States, Li review, we transcribe terminology employed by authors and Zhu [21] in China or Hildebrand and Van Kerm which reflects causal relationships even if cross-sectional [15] in Europe. Importantly, the strong version of Income databases are used or some endogeneity might be at play. Inequality Hypothesis and the weak version of Income Inequality Hypothesis are non-nested given that the weak The Absolute Income Hypothesis version considers the rank of individuals and an inter- From an early stage in the debate, the Absolute Income action term between the rank and the income inequal- Hypothesis states that the relationship between health and ities index whereas the strong version does not. Thus, income is positive and concave [29], meaning that peo- both versions can be valid when income inequalities in ple with higher incomes have better health outcomes, but a society are negatively associated to the health of all income inequalities have no direct effect on health. As a individuals, and more particularly the health of people result, the concavity of the relationship between individ- ranked at the lower end of the income distribution. How- ual income and health status is a necessary condition to ever, the authors previously mentioned focus mainly on assess the efficiency of redistributive policies, in which one of the versions in the best case (mainly on data transferring a given amount of money from rich people to from the United States), without comparing them. This poor people will result in an improvement of the average paper aims at filling these gaps by looking at the three health. hypotheses, using the same European data, in order to give The individual-level relation between income and more insight about efficient public policies which should health is specified as follows: be implemented in Europe. Finally, studying these three h = β + x β + x β + Z γ +  (1) i 0 i 1 2 i i hypotheses at the same time allows to highlight different where h represents the health status of individual i (objec- mechanisms between health and income. i tive or subjective measures); x is the income of individual In this paper, we test the three above hypotheses with i i; Z is a set of individual specific control variables ;and the Survey of Health, Ageing, and Retirement in Europe i is the error term coming from differences in individual (SHARE), using mainly the fifth wave of this survey i health. The concavity effect is legitimized if β is positive, (2015 release), as well as the pooled version of the sur- 1 ∂h vey in robustness. We use self-perceived health status β is negative, and > 0. ∂x as our health outcome. This type of subjective mea- A strong link between health and income has been sure is sometimes criticized but it is similar to the ones demonstrated in a large number of empirical studies, and used by Mackenbach et al. [26], Fiscella and Franks [13] a concave relationship between the two is found. Preston and Hildebrand and Van Kerm [15]. Furthermore, some [29] explains that the impact of additional income on authors show that these subjective measures are not mortality is greater among the poor than richer people. biased [1]. Lastly, even if this type of measure can be criti- Ettner [12], using three US surveys, finds that increases cized because of interpersonal comparison issues, authors in income improve mental and physical health but also prove that some econometric models tackle these prob- increase alcohol consumption. Then, Mackenbach et al. lems [22] (see “Robustness checks” subsection for some [26] show that a higher income is associated with bet- robustness checks in which we explicitly consider this ter self-assessed health in Europe. Using mortality rates, issue). Cutler et al. [10] conclude the same thing in the United The paper is organized as follows. “Literature review: States. Theodossiou and Zangelidis [31], using data on the relationship between income inequalities and health” individuals aged between 50 and 65 from six European section presents formally the three hypotheses that we will countries, find a positive but small effect of income on test empirically. “Method” section describes the SHARE health. More recently, Carrieri and Jones [8] analyze the datasetaswellasthe baseline econometricspecifica- effect of income on blood-based biomarkers and find a tion. In “Results” section we present the results and some positive and concave effect of income on health. robustness checks. “Conclusion” section concludes the paper. The strong version of Income Inequality Hypothesis Some researchers affirm that income inequalities in a Literature review: the relationship between society are equally important in determining individual income inequalities and health health status. The key difference between the Absolute Inequalities in health refer to the close relationship Income Hypothesis and the strong version of Income between health and membership in a group characterized Inequality Hypothesis stems from the fact that the latter Adeline and Delattre Health Economics Review (2017) 7:27 Page 3 of 18 explicitly considers the effect of income inequalities on from other people in societies where inequalities rise. health while the former only takes into account the con- Thus, reducing social spending turns into a decrease in cavity assumption between health and income. Mellor and life opportunities for poorer people and thus an increase Milyo [27] specifically define two versions of this hypoth- in inequalities (see also [14]). The second mechanism is esis: the strong version and the weak version. The strong that income inequalities lead to the erosion of the “fea- version of the Income Inequality Hypothesis implies that, tures of social organization that facilitate cooperation for whatever the level of income, the health of all individuals mutual benefit”. In other words, Kawachi and Kennedy in a society is equivalently affected by income inequali- [18] interpret this mechanism as the erosion of the “social ties in this society. In this way, both the well-off and poor capital”, corresponding to the set of collective resources people are impacted by income inequalities. These may an individual can put together. This may be the access to be a public bad for all members in a society since income public services, the feeling of security, the characteristics inequalities are a threat to the health of all individuals. of the relatives or the community solidarity (Grignon We can thus identify an individual effect (a micro part) et al.: Mesurer l’impact des déterminants non médicaux which is assimilated to the Absolute Income Hypothesis des inégalités sociales de santé, unpublished). Here we and an aggregate effect (a macro part) which corresponds focus on the solidarity argument. This one is important to the relationship between individual health and income for the maintenance of population health. Kawachi and inequalities in a society. Theoretically, the strong ver- Kennedy [18] made a study using the General Social sion of the Income Inequality Hypothesis is specified as Survey where each indicator of social capital (like the follows: degree of mistrust or levels of perceived reciprocity) was correlated with lower mortality rates. An increasing level h = β + x β + x β + δII + Z γ +  (2) ij 0 i 1 2 j i ij of mistrust between the members of a society was due which is an expansion of Eq. (1) with the introduction of to the development of the distance between the well-off’s II as a measure of income inequalities in a society j (cor- expectation and the ones of poorer people. Unfortunately responding to the macro part explained above); where h this result implies a growth of a latent social conflict. ij represents the health status of individual i in a society j. As a result, when health is associated to the erosion of This hypothesis has been empirically tested mainly on social capital, this seems to be towards the transition data from developed countries (principally in the United of social policies which are detrimental to poor people, States). Tests have been conducted at both the individual implying unequal political participation. A lower turnout level and the aggregate level. At the aggregate level, a at elections is perceived among states with low levels of interpersonal trust. These states are less likely to invest number of studies try to demonstrate an association in policies that ensure thesecurityofpoorerpeopleina between income inequalities and public health and the society. Finally less generous states are likely to provide results are contrasted [17, 25, 30]. At the individual level, less hospitable environments for these individuals. The Kawachi et al. [19], Kennedy et al. [20], and Fiscella and last mechanism is that income inequalities are correlated Franks [13] all find a negative association between income to unhealthiness through stressful social comparisons. inequalities and self-perceived health. However, Van In this case, a technique in anthropology called “cul- Doorslaer et al. [32] find no effect of income inequalities on an objective health measure, the McMaster health tural consensus analysis” is used to take into account utility index, derived from the self-perceived health the psychosocial effects of social comparisons. Indeed, status. Finally, other authors test the impact of income many communities have a common cultural model of the inequalities on malnutrition [33] or health service use standard of living. This technique involves interviewing [23] and find contrasted results. people and observing if individuals succeed in achieving The strong version focuses on the direct ties between the cultural model of lifestyle. This aspect can be seen as health and income inequalities. There are several poten- the satisfaction individuals have with their life. However, tial pathways through which income inequalities might it should be noticed and not forgiven that a possible endo- be negatively related to an individual’s health. Kawachi geneity issue can appear with this mechanism connected and Kennedy [18] summarize three plausible mechanisms to the life satisfaction of individuals. linking income inequalities to health. The first one is that disinvestment in human capital is linked to income The weak version of Income Inequality Hypothesis inequalities. In states with high income inequalities, edu- The second version of the Income Inequality Hypothesis cational outcomes are negatively impacted when a smaller is the weak one. According to this hypothesis, people who proportion of the state budget is spent on education which aremorelikelytohavepoorerhealthare theoneswho creates differences in education and thus in income. High feel more economically disadvantaged than their peers in a reference group. As a result, it specifically suggests that income disparities may translate into lower social spend- only the least well-off are hurt by income inequalities in ing because interests of richer persons begin to diverge Adeline and Delattre Health Economics Review (2017) 7:27 Page 4 of 18 a society. The damaging effect of these inequalities on throughout Europe to a sample of households with at health decreases with a person’s income rank. Indeed, for least one member who is 50 and older. These households an individual, stress and depression leading to illness may are re-interviewed every two years in the panel. SHARE be linked to the fact of having a low relative income when is part of a context of an ageing population. It is the compared to another person [9]. The main concern is thus European Commission which has identified the need for on the difficulties that an individual may face when he is scientific knowledge about ageing people in Europe. In situated at the bottom of the social ladder. Theoretically, fact, people of the European Innovation Partnership on the weak version of the Income Inequality Hypothesis is Active and Health Ageing project estimate that in 2050, specified as follows: one in three Europeans will be over 60 years old and one in ten will be over 85 years old. The SHARE survey was h = β + x β + x β + δII + θR (3) ij 0 i 1 2 j ij then constructed in the different European countries under the leadership of Professor Axel Börsch-Supan. + ηR ∗ II + Z γ + ij j i ij In addition, SHARE is harmonized with the Health and Retirement Study (in the United States - HRS) and the which is an expansion of Eq. (2) where we introduce R ij English Longitudinal Study of Ageing (UK - ELSA). as a person’s rank, and the interaction between inequal- ities and a person’s rank (R ∗ II ) to allow the effects The first wave (2004-2005, 27,014 individuals) and the ij j of income inequalities to vary by the relative income second one (2006-2007, 34,393 individuals) were used level in a society. The interaction term allows us to to collect data on health status, medical consumption, know how income inequalities are related to people with socio-economic status and living conditions. The 2008- lower levels of income, compared to other people. There- 2009 survey (Wave 3 - “SHARELIFE”) was extended to fore, this hypothesis suggests that the breadth of the life stories by collecting information on the history of the difference between rich people and poor ones accounts respondents. The number of participants increased from for the health. When testing this equation, δ underlines 12 countries in wave 1, to 15 (+ Ireland, Israel, Poland the strong version of the Income Inequality Hypothesis andCzech Republic)inwave2,and thethirdwavecon- whereas θ and η specifically refer to the weak version. tains information about 14 countries. The fourth wave Thus, if the three previous coefficients are significant and (2010-2011), is a return to the initial questionnaire of the have the right signs, then both the strong and the weak first two waves. It collects data from 56,675 individuals version are correct, meaning that everybody’s health is in 16 European countries. Finally, the fieldwork of the associated to income inequalities, and in particular people fifth wave of this survey was completed in 2013. The following countries are included in the scientific release who are at the lower end of the income distribution. On of 2015: Austria, Belgium, Switzerland, Czech Republic, the other hand, whether only δ (or θ and η respectively) is Germany, Denmark, Estonia, Spain, France, Israel, Italy, significant implies that only the strong version (resp. the Luxembourg, Netherlands, Sweden, and Slovenia. This weak version) is satisfied. wave contains the responses of 63,626 individuals. We As explained in the introduction, only few researches focus on the fifth wave [3] in order to have a great number focus on this hypothesis. Mellor and Milyo [27] use data of individuals who come from different countries. More- from the Current Population Survey and find no consis- over, in order to test and compare the three hypotheses tent association between income inequalities and individ- linking health and income, one has to use the same set of ual health. On the other hand, Li and Zhu [21], using data from China, find that income inequalities are detrimental observations (e.g. the fifth wave of the SHARE survey). for people who are at the lower end of the income hierar- We do not make our analysis using directly the pooled chy. Finally, Hildebrand and Van Kerm [15] also test the database since all the control explanatory variables are hypothesis that income inequalities may affect only the not available in each waves, which is a limitation of least well-off in a society using the European Community this database. Moreover, we also focus on the pooled Household Panel but find no evidence supporting it. database (waves 1, 2, 4 [4–6] and 5) in order to make our results more robust (the third wave is not considered in Method the pooled database since it does not contain the same The data information as the other ones). The survey The advantage of the SHARE database is that it has The Survey of Health, Ageing and Retirement in Europe many individual variables on health, socioeconomic sta- (SHARE) is a multidisciplinary and cross-national panel tus and income to perform this research. However, database of micro data on health, socio-economic status researchers should be also aware of the potential dis- advantage of this database. Indeed, Börsch-Supan et al. and social and family networks of more than 123,000 [7] explain that in some waves there are a relative low individuals aged 50 and over from many European coun- response rates and moderate levels of attrition (even tries and Israel [7]. Since 2004, SHARE asks questions Adeline and Delattre Health Economics Review (2017) 7:27 Page 5 of 18 though the overall response rate is high compared to other As a result, one of the solution is to use the Theil index European and US surveys ) which are due to the eco- which measures income inequalities. The Theil index is: nomic crisis faced by some countries, implying a decrease 1 y y i i in theparticipation rates. Duetothisattrition,wethus Theil = ln (5) N y ¯ y ¯ focus on the fifth wave of this survey instead of the pooled database. Nonetheless, we present the results using the pooled database as a robustness test. where y ¯ is the mean income per person (or expenditure per capita). In order to normalize the Theil index to vary Indexes for the measurement of income inequalities 6 between zero and one, we divide it by ln(N ). It mea- In this study, we want to underline the effects of income sures a “distance” of the real population and the “ideal” inequalities on health and this is why we need a measure- egalitarian state where everyone have the same income. ment of income inequalities. The Gini coefficient, as well Since the Gini coefficient does not take into account the as the Theil index are two well-known indexes which can income distribution, most of the following tables of results be used. will be displayed using the Theil index. Algebraically, the Gini coefficient is defined as half of the arithmetic average of the absolute differences between Descriptive statistics - an overview all pairs of incomes in a population, and then the total In this paper, the data used are from the fifth wave of the is normalized on mean income. If incomes in a popula- SHARE survey. This wave includes responses from 63,626 tion are distributed completely equally, the Gini value is respondents aged 50 and over, living in 15 different coun- zero, and if one person has all the incomes in a society, the tries. Thus, this survey aims to provide information on Gini is one. The Gini coefficient can be illustrated through health, income, activities and other features of the elderly. the Lorenz curve. However, the Gini coefficient does not In one hand, the variable of interest is the health which is take into account the income distribution since different defined in the database as the self-perceived health status. Lorenz curves may correspond to the same Gini index. In Individuals are asked to classify their health using ordered other words, it does not distinguish between inequalities qualitative labels from “poor” to “excellent. The Fig. 1 in low income group and high income ones. Formally, the characterizes the distribution of the health variable among Gini coefficient is: individuals aged 50 and older by gender for all countries. As we can see the majority of inhabitants reports being in 2 iy N + 1 Gini = − (4) a good health. In the other hand, one of our main deter- N y N minant of health is the income. This variable can be seen with y representing the income of the population sorted as a proxy for well-being, that is to say a factor which and ranked, from the lowest decile group to the top decile allows individuals to improve the living standards. In the group, and N representing the total population. database, it corresponds to the sum of individual imputed Fig. 1 Self-perceived health in Europe Adeline and Delattre Health Economics Review (2017) 7:27 Page 6 of 18 income for all household components. Figure 2 shows the distribution of income of people aged 50 and over in the fifth wave where the mean is about 36,000e.Moreover, the income inequality hypothesis includes an indicator for the measurement of income inequalities (see Fig. 3). In this paper, we use either the Gini index or the Theil index. The mean of the Gini index in Europe is 0.39 which cor- responds to a rather egalitarian society. The mean of the Theil index in Europe is 0.33 which is also rather egali- tarian. In our analysis we include others variables such as the age, the marital status, the education, the job situation, dummies for the countries and the gender, and the GDP of the countries (see Tables 2, 3, 4 and 5 in the Appendix for further information). Finally, the pooled data (waves Fig. 3 Income inequalities indexes in Europe 1, 2, 4 and 5) contains 181,708 observations, where each individual is present on average 2.9 years in the panel. is equalto1,2,3,4or 5for “poor”,“fair”, “good”, “very The ordered probit model good” or “excellent” with this probability: To model the association between self-perceived health and other socioeconomic status and test the hypothe- P(y = j|x) = F(μ − x β) − F(μ − x β) (8) j i j−1 i ses, we use an ordered probit specification. When the The interval decision rule is: self-perceived health status outcome is denoted as h ,the model can be stated as: ∗ 1. h = 1 if h ≤ μ ; i 1 2. h = 2 if μ < h ≤ μ ; i 1 2 h = j iff μ < h ≤ μ,(6) i j−1 j i 3. h = 3 if μ < h ≤ μ ; i 2 3 for j = 1, 2, 3, 4, 5 4. h = 4 if μ < h ≤ μ ; i 3 4 The latent variable specification of the model that we 5. h = 5 if h >μ . i 4 estimate can be written as: In this model, the threshold values (μ , μ , μ , μ )are 1 2 3 4 h = x β +  (7) i i unknown. We do not know the value of the index neces- sary to shift from very good to excellent. In theory, the where h is a latent variable which underlies the self- threshold values are different for everyone. reported health status ; x is a set of observed socioe- conomic variables; and  is an individual-specific error Results term, which is assumed to be normally distributed. Economic results and discussion In this data, the latent outcome h is not observed. Table 1 reports coefficient estimates for all estimated Instead, we observe an indicator of the category in which ordered probit models when income inequalities are mea- the latent indicator falls. As a result the observed variable sured using the Theil index. The fifth wave gives us access to 63,626 observations and we also display results of the pooled database for sake of robustness (see Table 6 in the Appendix section). Results in the first column reports theestimated coefficients forthe absoluteincomehypoth- esis while results in columns two and three provide tests of both the strong version and the weak version of the income inequality hypothesis. Coefficients of individual income and income squared provide support for all the hypotheses that there is a positive and concave relationship between income and self-perceived health status. Indeed, coefficients associ- ated to the income variable are all positive and significant and coefficients associated to the income squared variable are all negative and significant. This implies that higher income is related to a better health outcome. As a result, Fig. 2 Distribution of income in Europe the absolute income hypothesis is verified. Concerning Adeline and Delattre Health Economics Review (2017) 7:27 Page 7 of 18 Table 1 Results of the ordered probit regressions for Wave 5 Variables Absolute income IIH Hypothesis Strong version Weak version ∗∗∗ ∗∗∗ ∗∗∗ Income 1.84e-06 1.84e-06 1.89e-06 (1.22e-07) (1.20e-07) (1.44e-07) ∗∗∗ ∗∗∗ ∗∗∗ Income squared −2.06e-13 −2.04e-13 −2.09e-13 (1.55e-14) (1.50e-14) (1.73e-14) Quintiles of income: Reference - Q5 ∗∗∗ Quintile 1 −0.258 (0.029) ∗∗∗ Quintile 2 −0.201 (0.028) ∗∗∗ Quintile 3 −0.115 (0.027) ∗∗∗ Quintile 4 −0.053 (0.026) ∗∗∗ ∗∗∗ Index of inequalities (II) - Theil −0.403 −0.838 (0.024) (0.049) Interaction quintile 1 and II 0.115 (0.069) Interaction quintile 2 and II 0.114 (0.068) Interaction quintile 3 and II 0.023 (0.068) Interaction quintile 4 and II 0.062 (0.068) ∗∗∗ ∗∗∗ GDP 1.99e-06 0.0001 (4.53e-07) (0.049) ∗∗∗ ∗∗∗ ∗∗∗ Age 0.037 0.019 0.037 (0.006) (0.006) (0.006) ∗∗∗ ∗∗∗ ∗∗∗ Age squared −0.0004 −0.0003 −0.0004 (0.00004) (0.0004) (0.00004) ∗∗∗ ∗∗∗ ∗∗∗ Years of education 0.034 0.028 0.026 (0.001) (0.001) (0.001) Gender = 1 if women 0.003 0.005 0.007 (0.009) (0.009) (0.009) Marital Status: Reference - Married Registered partnership −0.042 −0.006 0.058 (0.035) (0.035) (0.035) ∗∗ ∗∗ Married, not living with spouse −0.094 0.004 −0.076 (0.039) (0.039) (0.039) ∗∗∗ Never married −0.071 0.023 0.023 (0.019) (0.019) (0.019) ∗∗∗ ∗∗∗ ∗∗ Divorced −0.045 0.068 0.032 (0.016) (0.018) (0.015) ∗ ∗∗∗ Widowed −0.024 0.055 0.015 (0.015) (0.014) (0.014) Job Situation: Reference Retired ∗∗∗ ∗∗∗ ∗∗∗ Employed 0.253 0.224 0.246 (0.014) (0.014) (0.014) ∗∗∗ ∗∗∗ ∗∗∗ Unemployed −0.212 −0.103 −0.176 (0.028) (0.028) (0.028) ∗∗∗ ∗∗∗ ∗∗∗ Permanently sick −1.25 −1.069 −1.207 (0.026) (0.026) (0.026) ∗∗∗ ∗∗∗ ∗∗∗ Home-maker −0.059 −0.064 −0.056 (0.017) (0.017) (0.017) ∗∗∗ ∗∗∗ ∗∗∗ Other −0.236 −1.169 −0.207 (0.031) (0.031) (0.031) Mechanisms IIHs: st ∗∗∗ 1 : % Health expenditure in GDP 0.077 (0.003) nd ∗∗∗ 2 : Received help from others −0.179 (0.006) nd ∗∗∗ 2 bis: Given help from others 0.001 (0.0001) rd ∗∗∗ 3 : Life satisfaction 0.216 (0.003) Cut-point μ −0.474 0.899 −0.428 (0.216) (0.219) (0.215) Adeline and Delattre Health Economics Review (2017) 7:27 Page 8 of 18 Table 1 Results of the ordered probit regressions for Wave 5 (Continued) Cut-point μ 0.615 2.076 0.632 (0.216) (0.219) (0.215) Cut-point μ 1.746 3.261 1.728 (0.216) (0.219) (0.215) Cut-point μ 2.592 4.133 2.548 (0.216) (0.219) (0.215) ME at mean of absolute income on: ∗∗∗ ∗∗∗ ∗∗∗ Pr(Poor health) −2.84e-07 −2.58e-07 −3.02e-07 (1.92e-08) (1.71e-08) (2.32e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Fair health) −3.06e-07 −2.97e-07 −3.24e-07 (2.05e-08) (1.95e-08) (2.49e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Good health) 8.80e-08 6.65e-08 9.56e-08 (6.44e-09) (4.97e-09) (7.80e-09) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Very good health) 2.65e-07 2.55e-07 2.79e-07 (1.78e-08) (1.68e-08) (2.14e-08) ∗∗∗ ∗∗∗ ∗∗∗ Pr(Excellent health) 2.37e-07 2.34e-07 2.51e-07 (1.59e-08) (1.54e-08) (1.92e-08) For AIH, dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant. Standard deviations are in parentheses, below the coefficients. income inequalities, coefficients on the Theil index in inequality hypothesis. Concerning the two other interac- columns two and three are negative and significantly dif- tion terms (third and fourth quintiles, representing people ferent from zero. This supports evidence of the strong at the middle and almost top of the income distribution), version of income inequality hypothesis stating that an coefficients are not statistically significant meaning that increase in income inequalities is detrimental to all mem- middle and higher income people are not affected at all bers of a society, i.e. income inequalities and health are by an increase in income inequalities. This claim does not negatively related. Indeed concerning this index, zero rep- support the weak version because this hypothesis states resents an egalitarian state, thus the negative relationship that people at the lower end are the most affected by an between self-perceived health and the indicator of income increase in income inequalities compared to people at the inequalities is in line with health being better if the index is top of the income distribution. As a result, higher income low. However, results in column three do not give support people should also be affected by income inequalities (at to the weak version of income inequality hypothesis which a lower rate). Our qualitative results suggest that for low- states that inequalities are more detrimental to the least income individuals, an increase in income inequalities well-off in a society. Indeed, we introduce individual rank in their country is positively related to report a better (by country) and an interaction term between the rank health status. Furthermore, for higher income individuals, and the index of income inequalities to allow a variation an increase in income inequalities in their country is not between income level and the effect of income inequali- related to report neitherabetter noralowerhealthstatus. ties. In the specification, we choose to follow the frame- To conclude, our results do not support the weak version work of Mellor and Milyo [27] who introduced interaction of income inequality hypothesis, but it further invalidates terms between the measurement of income inequalities this weak version because our qualitative results quite and dummies variables based on quintiles of income (1 for claim the opposite. the lowest income group and 5 for the highest, which is Regarding the mechanisms of Kawachi and Kennedy a proxy for the rank). In other words, interaction terms [18] (Table 1, column two), the disinvestment in human indicate the effect of aggregate income inequalities (at capital(firstmechanism)ischaracterized by theper- the country level) on self-perceived health status between centage of health expenditure in the GDP. The coeffi- individuals situated at different levels of the income distri- cient associated is positively correlated to health meaning bution. Concerning the first two interaction terms (II ∗Q1 that when governments increase health spending, this and II ∗ Q2), these indicate the effect of aggregate income has a positive effect on individual health. For the sec- inequalities (at the country level) on self-perceived health ond mechanism, we want to illustrate the interaction status between the poorest individuals (situated at the between individuals to represent the erosion of social cap- lower end of the income distribution) and the richest ital. As a result, we choose a variable from the SHARE ones (reference category corresponding to individuals sit- survey: “received help from others”. The coefficient asso- uated at the top of the income distribution). These coef- ciated to this variable is negative and significant. We ficients are positive and statistically significant, meaning can explain this negative association by saying that peo- that for the poorest individuals (compared to more well- ple who are in bad health are the ones who receive off individuals), an increase in income inequalities in their help. In order, to legitimize this explanation, we also country increases self-perceived health status, which is do the estimation with the “reverse variable”: “given in contradiction with the weak version of the income help to others”. In this case, the coefficient is positive Adeline and Delattre Health Economics Review (2017) 7:27 Page 9 of 18 and significant proving that people in good health offer the impact of income on the probability to report a good their help. Then, the last mechanism is about social health status. For almost all the distribution, when income comparisons. The coefficient associated to this variable raises, the probability increases. Then, graphs 4d and 4e (“life satisfaction”) is positively linked to health which are more conclusive. Indeed, graph 4d gives the impact implies that when individuals are satisfied with their life, of income on the probability to have a very good health. they also report having a good health. For more than 99% of the income distribution, this impact In sum, our baseline specifications provide evidence is positive and decreasing, which might support the con- of a statistically significant association between income, cavity assumption. Finally, graph 4e gives the impact of income inequalities and health since results are robust to income on the probability of reporting an excellent health model specifications. status. As previously, when income increases, the proba- bility to have an excellent health increases. However, when Robustness checks we look at people with very high incomes ,thisimpactis As a sake of robustness, we also make our entire analysis greater than for the majority of individuals. using the pooled database (see Table 6 in the Appendix Finally, it is important to investigate the robustness of section) and the results are very similar to the ones our results by taking into account the subjective nature obtained with the fifth wave of the survey. of the self-perceived health status. Indeed, our baseline To give more support to the concavity assumption, we specification depends on a dependent variable which is compute, for all three hypotheses, the marginal effects at subjective. Self-reported measures give a good amount mean of income on the five outcomes. Results, reported of information about individual health since people sum- at the end of Table 1, are all significant. On one hand, marize all the health information they have from their for the first two outcomes, income has a negative effect practitioners (general practitioners and specialists) and on the probability to report either a poor health or a fair from what they feel [1]. The use of this measure in our health status. On the other hand, there is a positive effect specification raises the problem of interpersonal com- of income on the probability to report being in a good, parisons between people aged 50 and over (“Is the way very good and excellent health (outcomes three to five). I consider “good health” the same as you consider this These results are obtained following the ordered probit health commodity?”. Empirical studies on the relationship regressions of the three hypotheses, where the quadratic between health, income and income inequalities com- effect of income is investigated (see Eqs. 1, 2 and 3). These monly use ordered probit models where the thresholds results do not validate the concavity assumption but they are constant by assumption. However, one limit is that it restricts the marginal probability effects. In fact the dis- do show the increasing effect of income on self-perceived health status. We also plot the average marginal effect tributional effects are restricted by the specific structure. of income on each outcome for all individuals with a Then, another limit is that additional individual hetero- confidence interval, in order to give more support to geneity between individual realizations is not allowed by the concavity effect in the three hypotheses (see Fig. 4). the distributional assumption. Thus, Boes and Winkel- We restrict ourselves to individuals who earn less than mann [2] and Jones and Schurer [16] both give a solution 200,000e per year (which corresponds to more than 99% to these issues with the use of the generalized ordered pro- of the distribution, see Table 4 in the Appendix section bit model since it is based on a latent threshold where the for further information on the distribution of income). thresholds themselves are linear function of the explana- The following graphs (Fig. 4) concern the absolute income tory variables. In other words, previous thresholds of Eq. 8 hypothesis. Graph4agives theimpactofincomeonthe are now computed by selecting individual characteristics probability to report a poor health. This impact is negative so that they depend on covariates: (y-axis is negative), meaning that when income raises, the μ =  μ + x γ (9) ij j j probability decreases. In addition, the negative impact is stronger for the majority of the population than for indi- where γ is a vector of response specific parameters. We viduals who earn very high incomes. In other words, for have: low incomes, in absolute terms, an additional increase in income has a larger impact on the probability of report- μ = μ ∀ ∈ C (10) ij j i j ing a poor health than for very high income. This is a low where C is the class. With this model, the probabilities support for the concavity assumption. Graph 4b gives the are: impact of income on the probability of reporting a fair health status. Conclusion are similar to the ones of graph P(y = j|x) = F( μ − x β ) − F( μ − x β ) (11) j i j j−1 i j−1 4a since the effect is negative. The slight decreases of the curve at the beginning does not impact the conclusion and Now, the effects of covariates on the log-odds are can be related to large confidence intervals. Graph 4c gives category-specific and this model allows to have more Adeline and Delattre Health Economics Review (2017) 7:27 Page 10 of 18 Fig. 4 Average marginal effects of income on health - Absolute Income Hypothesis. a Probability to report a poor health; b Probability to report a fair health; c Probability to report a good health; d Probability to report a very good health; e Probability to report an excellent health heterogeneity across individuals. Results concerning the inequalities is negative and significant which is in line generalized ordered probit model are similar to those with the strong version of the income inequality hypoth- obtained from the ordered probit model. All the effects esis. Then, concerning the interaction terms, these are are estimated around each four cut-points (from poor to not significant for all quintile groups which do not justify fair, from fair to good, from good to very good, and from the weak version of income inequality hypothesis. Finally, very good to excellent). For all the hypotheses (absolute adding some heterogeneity in this model and taking into income hypothesis - Appendix: Table 7, income inequal- account the issues of interpersonal comparisons do not ity hypothesis, both versions - Tables 8 and 9 in the modify our previous results. Appendix part), the coefficients associated to the vari- ables of interest (income and income squared) do not Conclusion change significantly in comparison to the results with the In this study we underline the hypotheses through which ordered probit model. Results are consistent (either with health is associated to income and income inequalities. the Theil index or the Gini coefficient for the income The aim of this paper is to empirically investigate the evi- inequality hypothesis) as this is proved in previous study dence for the absolute income hypothesis and both the [22]. In fact, in the four cut-points, the results legitimize strong and the weak versions of the income inequality the concavity assumption of income since the coefficients hypothesis for people aged 50 and over in Europe, using are statistically significant. Moreover, the index of income data from theSHARE survey.Indeed,wereviewthe Adeline and Delattre Health Economics Review (2017) 7:27 Page 11 of 18 relationship on income-related health inequalities where good effects through individual levels of health. There will we mention the literature as well as the theoretical and be like a virtuous circle in which incomes influence the statistical tools needed to carry out this research. Then health status (improving the production possibilities of we present the data used and some descriptive statistics. the economy can be achieved by improving the health) Finally we show the model specification, the results of the which in turn affects the income. three hypotheses and some robustness tests. This whole work, both the literature study and the establishment of Endnotes various models led us to estimate different assumptions on In this way, redistributing income from rich people to the relationship between health and income. This study is poor people will have an important and positive impact on one of the first analyzing this relationship through differ- the health of the poorer people, whereas the richer ones ent hypotheses at the same time using the SHARE survey which is a rich database, containing a lot of information will experience a small decrease in their health. on elderly people and countries simultaneously. Such as age, gender, number of years of education, We find evidence supporting the absolute income marital status and the job situation. It can also contain hypothesis which states that people with higher incomes countries dummies variables. have better health outcomes. We also find evidence See http://ec.europa.eu/ for an explanation of the supporting the strong version of income inequality European Innovation Partnership on Active and Healthy hypothesis which argues that inequality affects all mem- bers in a society equivalently. In this hypothesis, we find Ageing - A Europe 2020 initiative. that when there are high income inequalities in a country, After wave four was completed, the average retention people aged 50 and over feel less healthy. However, we do rate over the year was 81%. not find evidence supporting the weak version of income For instance, if 50 percent of the population has no inequality hypothesis which states that only the least income and the other half has the same income, the Gini well-off are hurt by income inequalities in a society. This index is 0.5. The same result can be found with the fol- hypothesis underlines the fact that income inequalities are more detrimental for the health of people with low lowing analysis which is less unequal. On one hand, 25 incomes. Our qualitative results suggest that for low- percent of total income is shared in the same way by income individuals, an increase in income inequalities in 75 percent of the population, and on he other hand, the their country is positively related to report a better health remaining 25 percent of the total income is divided by the status. Furthermore, for higher income individuals, an remaining 25 percent of the population. increase in income inequalities in their country is not It is this normalized index that we use hereafter and related to report either a better or a lower health status. One limitation is the used of cross-sectional data with- that we name the Theil index. 7 ∗ out investigating possible endogeneity issues. Thus our Once h crosses a certain value you report fair, then results highlight statistical associations rather than causal poor, then good, then very good, then excellent health. effects. Finally, by implementing the generalized ordered Results associated to the Gini coefficient are not pro- probit, we control for potential problems of interpersonal vided here but they are very similar and available upon comparisons and the results are very similar to those request. found with the ordered probit model. Results concerning the hypotheses are consistent with Source: OECD website. the concavity assumption of income on health. Extension We look at the average individual of the database and would be to highlight causal effects, using other meth- compute the marginal effects. ods, in order to support some political implication. In fact, We do not include the ones for the income inequality what is important in determining the health status is more hypothesis (both versions) since the results are very simi- how income is distributed in a society and less the overall lar and do not change the main conclusion, but these are health of this society. As a result, the more equally income is distributed, the better the overall health in this soci- available upon request. ety. Concerning political implication, one way to improve In this case, people with very high incomes are indi- health might be to take measures using the redistribution viduals who earn more than 150,000e per year, corre- of incomes as a lever. In fact, Lynch et al. [24] argue that, sponding to less than 2% of the sample. redistributive fiscal and tax policies will help the govern- ments to achieve better population health. Deaton [11] explains that if income inequalities affect health, transfer Appendix policies that affect the distribution of incomes would have Descriptive Statistics Adeline and Delattre Health Economics Review (2017) 7:27 Page 12 of 18 Table 2 Descriptive statistics of the variables Variables Mean Standard deviation Minimum Maximum Health Self-perceived health status (N = 63626) 2.85 1.09 1 5 Inequalities Gini per country 0.39 0.05 0.31 0.48 Theil per country 0.33 0.19 0.16 0.82 Other Variables Income 36,621.21 71,863.78 2 1.00e+07 GDP per country (2013 - Dollar US/capita) 39,726.43 11,543.57 26,160.08 92,781.41 Education 11.12 4.28 1 25 Age 67.12 10.06 50 103 Table 3 Detailed descriptive statistics for the health Health Percentage of people Poor (1) 10.81% Fair (2) 27.01% Good (3) 36.52% Very Good (4) 17.58% Excellent (5) 8.18% Table 4 Detailed descriptive statistics for income Distribution Income 5% 3,828.99 25% 12,446 50% 24,659.55 75% 46,200 95% 103,897.2 Table 5 Detailed descriptive statistics for the countries Country Percentage of people* GDP - 2013** Indexes of inequality*** Theil index Gini index Austria 6.54% 45 132.54 0.1762 0.3222 Germany 8.71% 43 282.31 0.2234 0.3672 Sweden 7.06% 44 585.87 0.1672 0.3183 Netherlands 6.42% 46 749.31 0.2152 0.3543 Spain 9.75% 33 111.45 0.2521 0.3813 Italy 6.88% 34 836.43 0.373 0.4239 France 6.86% 37 617.06 0.8224 0.4772 Denmark 6.37% 43797.23 0.1578 0.3138 Switzerland 4.62% 56 896.91 0.2144 0.3554 Belgium 8.66% 41 863.94 0.3849 0.4545 Czech Republic 8.7% 28 962.64 0.2123 0.3512 Luxembourg 2.5% 92 781.4 0.2649 0.3979 Israel 3.56% 32 504.72 0.2475 0.3906 Slovenia 4.51% 28 675.43 0.3696 0.451 Estonia 8.88% 26 160.08 0.6816 0.4497 *: From each country in the full sample **: Gross Domestic Product, Total dollar US/capita ***: Values Adeline and Delattre Health Economics Review (2017) 7:27 Page 13 of 18 Additional Econometric Results Table 6 Results of the ordered probit regressions for the pooled database Variables Absolute Income IIH Hypothesis Strong Version Weak Version ∗∗∗ ∗∗∗ ∗∗∗ Income 1.41e-06 1.94e-06 1.16e-06 (4.74e-08) (4.34e-08) (4.76e-08) ∗∗∗ ∗∗∗ ∗∗∗ Income squared −1.78e-13 −2.39e-13 −1.46e-13 (1.14e-14) (1.13e-14) (1.12e-14) Quintiles of income: Reference - Q5 ∗∗∗ Quintile 1 −0.379 (0.019) ∗∗∗ Quintile 2 −0.288 (0.019) ∗∗∗ Quintile 3 −0.184 (0.019) ∗∗∗ Quintile 4 −0.115 (0.018) ∗∗∗ ∗∗∗ Index of inequalities (II) - Theil −0.473 −0.567 (0.018) (0.038) Interaction quintile 1 and II 0.121 (0.053) Interaction quintile 2 and II 0.054 (0.053) Interaction quintile 3 and II −0.012 (0.052) Interaction quintile 4 and II 0.053 (0.052) Interaction quintile 5 and II Reference ∗∗∗ ∗∗∗ GDP 0.0002 0.0002 (3.03e-07) (3.06e-07) ∗∗∗ ∗∗∗ ∗∗∗ Age −0.014 −0.018 −0.015 (0.003) (0.003) (0.003) ∗∗∗ ∗∗ ∗∗∗ Age squared −0.0001 −0.0001 −0.0006 (0.00002) (0.0002) (0.00002) ∗∗∗ ∗∗∗ ∗∗∗ Years of education 0.021 0.019 0.017 (0.001) (0.0005) (0.001) ∗∗∗ ∗∗∗ ∗∗∗ Gender = 1ifwomen −0.055 −0.057 −0.050 (0.005) (0.005) (0.005) Marital Status: Reference - Married ∗∗∗ ∗ Registered partnership −0.060 −0.030 −0.026 (0.017) (0.017) (0.017) ∗∗∗ ∗∗∗ ∗∗∗ Married, not living with spouse −0.098 −0.087 −0.091 (0.009) (0.009) (0.009) ∗∗∗ ∗∗∗ ∗∗ Never married −0.127 −0.108 −0.027 (0.014) (0.013) (0.014) ∗∗∗ ∗∗∗ Divorced −0.079 −0.062 0.016 (0.011) (0.011) (0.011) ∗∗∗ ∗∗∗ ∗∗∗ Widowed −0.046 −0.055 0.026 (0.009) (0.009) (0.009) Waves: Reference - Wave 5 ∗∗∗ ∗∗∗ ∗∗∗ Wave 1 0.139 0.431 0.469 (0.009) (0.009) (0.009) ∗∗∗ ∗∗∗ ∗∗∗ Wave 2 0.094 0.247 0.272 (0.009) (0.009) (0.009) ∗∗∗ Wave 4 −0.024 −0.001 0.003 (0.006) (0.006) (0.006) Cut-point μ −2.494 −1.960 −1.976 (0.104) (0.104) (0.105) Cut-point μ −1.46 −0.952 −0.962 (0.104) (0.105) (0.105) Cut-point μ −0.378 0.106 0.102 (0.104) (0.105) (0.104) Cut-point μ 0.455 0.919 0.919 (0.104) (0.104) (0.105) For AIH, dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant. Standard deviations are in parentheses, below the coefficients Adeline and Delattre Health Economics Review (2017) 7:27 Page 14 of 18 Table 7 Absolute Income Hypothesis - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.99e-06 *** 2.25e-06 *** 3.68e-06 *** 3.81e-06 *** (2.76e-07) (2.00e-07) (2.44e-07) (4.44e-07) Income squared -2.11e-13 *** -7.96e-13 *** -3.26e-13 *** -5.41e-12 *** (2.90e-14) (1.17e-13) (4.71e-13) (1.55e-12) Age 0.037 *** 0.037 *** 0.026 *** 0.029 *** (0.01) (0.008) (0.009) (0.012) Age squared -0.0004 *** -0.0004 *** -0.0004 *** -0.0003 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.031 *** 0.038 *** 0.036 *** 0.024 *** (0.002) (0.001) (0.001) (0.002) Gender = 1 if women 0.066 *** -0.014 -0.005 -0.002 ** (0.016) (0.012) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership -0.063 -0.093 ** 0.029 -0.027 (0.069) (0.046) (0.045) (0.057) Married, not living with spouse -0.251 *** -0.112 ** -0.0001 0.118 * (0.062) (0.049) (0.053) (0.069) Never married -0.048 -0.068 *** -0.038 -0.065 * (0.032) (0.024) (0.026) (0.035) Divorced -0.157 *** -0.059 *** 0.05 *** 0.06 ** (0.026) (0.019) (0.021) (0.027) Widowed -0.017 -0.026 0.002 -0.015 (0.021) (0.017) (0.02) (0.029) Job Situation: Retired Reference group Employed 0.398 *** 0.312 *** 0.203 *** 0.174 *** (0.029) (0.019) (0.019) (0.025) Unemployed -0.222 *** -0.191 *** -0.233 *** -0.126 ** (0.047) (0.035) (0.038) (0.053) Permanently sick -1.196 *** -1.268 *** -1.307 *** -0.963 *** (0.033) (0.038) (0.054) (0.076) Home-maker -0.088 *** -0.052 ** -0.047 * -0.006 (0.029) (0.022) (0.025) (0.035) Other -0.354 *** -0.173 *** -0.145 *** -0.017 (0.041) (0.037) (0.046) (0.064) Dummies for countries are included but not reported, and available upon request ***: 1% significant; **: 5% significant; *: 10% significant 1 to 2: Poor to Fair; 2 to 3: Fair to Good; 3 to 4: Good to VG; 4 to 5: VG to Excellent Adeline and Delattre Health Economics Review (2017) 7:27 Page 15 of 18 Table 8 IIH, strong version - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.75e-06 *** 2.34e-06 *** 3.89e-06 *** 3.20e-06 *** (2.69e-07) (1.97e-07) (2.38e-07) (4.42e-07) Income squared -1.89e-13 *** -8.28e-13 *** -3.75e-12 *** -5.18e-12 *** (2.82e-14) (1.18e-13) (4.72e-13) (1.60e-12) Index of inequalities (Theil) -0.095 ** -0.369 *** -0.7389 *** -0.4746 *** (0.041) (0.031) (0.035) (0.048) Mechanisms: 1st: % Health exp. in the GDP 0.059 *** 0.087 *** 0.073 *** 0.082 *** (0.005) (0.004) (0.004) (0.006) 2nd: Received help from others -0.214 *** -0.193 *** -0.134 *** -0.089 *** (0.009) (0.008) (0.009) (0.013) 2nb bis: Given help to others 0.001 *** 0.001 *** 0.001 *** 0.001 *** (0.0001) (0.0001) (0.0001) (0.0001) 3rd: Life satisfaction 0.195 *** 0.215 *** 0.239 *** 0.238 *** (0.004) (0.003) (0.004) (0.006) GDP 2.52e-06 *** 1.41e-06 ** -4.87e-07 5.94e-07 (8.66e-07) (6.04e-07) (6.36e-07) (8.72e-07) Age 0.019 * 0.004 0.013 0.019 * (0.01) (0.008) (0.009) (0.012) Age squared -0.0003 *** -0.0002 *** -0.0003 *** -0.0003 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.025 *** 0.029 *** 0.028 *** 0.021 *** (0.002) (0.001) (0.0014) (0.0018) Gender = 1 if women 0.069 *** -0.018 -0.003 -0.0004 (0.016) (0.012) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership -0.023 -0.053 0.034 0.014 (0.071) (0.047) (0.045) (0.058) Married, not living with spouse -0.131 ** 0.005 0.091 * 0.122 * (0.065) (0.051) (0.054) (0.072) Never married 0.033 0.023 0.064 ** 0.001 (0.034) (0.025) (0.027) (0.036) Divorced -0.046 * 0.062 *** 0.166 *** 0.122 *** (0.028) (0.021) (0.022) (0.028) Widowed 0.053 ** 0.069 *** 0.076 *** 0.022 (0.023) (0.018) (0.022) (0.031) Job Situation: Retired Reference group Employed 0.344 *** 0.225 *** 0.177 *** 0.176 *** (0.03) (0.019) (0.019) (0.025) Unemployed -0.141 *** -0.097 *** -0.11 *** 0.012 (0.048) (0.035) (0.039) (0.054) Adeline and Delattre Health Economics Review (2017) 7:27 Page 16 of 18 Table 8 IIH, strong version - Generalized ordered probit (Wave 5) (Continued) Permanently sick -1.016 *** -1.121 *** -1.098 *** -0.744 *** (0.034) (0.034) (0.056) (0.084) Home-maker -0.074 *** -0.033 -0.076 *** -0.044 (0.029) (0.022) (0.025) (0.035) Other -0.299 *** -0.114 *** -0.09 * 0.048 (0.043) (0.038) (0.048) (0.067) ***: 1% significant; **: 5% significant; *: 10% significant 1 to 2: Poor to Fair; 2 to 3: Fair to Good; 3 to 4: Good to VG; 4 to 5: VG to Excellent Table 9 IIH, weak version - Generalized ordered probit (Wave 5) Variables Health commodities 1to2 2to3 3to4 4to5 Income 1.97e-06 *** 3.03e-06 *** 5.92e-06 *** 7.65e-06 *** (3.06e-07) (2.43e-07) (3.15e-07) (6.10e-07) Income squared -2.09e-13 *** -1.14e-12 *** -6.03e-12 *** -1.60e-11 *** (3.17e-14) (1.25e-13) (5.21e-13) (1.92e-12) Index of inequalities (Theil) -0.319 *** -0.79 *** -1.077 *** -0.899 *** (0.101) (0.065) (0.065) (0.084) Quintile 1 -0.145 *** -0.195 *** -0.003 0.07 (0.055) (0.039) (0.043) (0.059) Quintile 2 -0.099 * -0.159 *** -0.014 0.079 (0.054) (0.038) (0.039) (0.059) Quintile 3 -0.061 -0.043 0.018 0.025 (0.054) (0.037) (0.037) (0.047) Quintile 4 -0.012 -0.02 0.055 0.023 (0.056) (0.036) (0.034) (0.043) Quintile 5 Reference group Interaction quintile 1 and II -0.204 * 0.079 -0.039 0.084 (0.12) (0.088) (0.107) (0.147) Interaction quintile 2 and II -0.162 0.097 0.048 0.029 (0.123) (0.087) (0.101) (0.138) Interaction quintile 3 and II -0.163 -0.048 -0.013 0.144 (0.125) (0.088) (0.098) (0.129) Interaction quintile 4 and II -0.058 0.066 0.001 0.098 (0.132) (0.088) (0.093) (0.124) Interaction quintile 5 and II Reference group GDP 0.0001 *** 9.96e-06 *** 3.83e-06 *** 2.17e-06 *** (8.30e-07) (6.31e-07) (6.99e-07) (9.91e-07) Age 0.034 *** 0.023 *** 0.029 *** 0.034 ** (0.01) (0.008) (0.008) (0.011) Age squared -0.0004 *** -0.0003 *** -0.0004 *** -0.0004 *** (0.0001) (0.0001) (0.0001) (0.0001) Years of education 0.025 *** 0.029 *** 0.028 *** 0.022 *** (0.002) (0.001) (0.001) (0.002) Adeline and Delattre Health Economics Review (2017) 7:27 Page 17 of 18 Table 9 IIH, weak version - Generalized ordered probit (Wave 5) (Continued) Gender = 1 if women 0.066 *** -0.016 0.0004 0.007 (0.015) (0.011) (0.012) (0.016) Marital Status: Married, living with spouse Reference group Registered partnership 0.053 0.023 0.075 * 0.049 (0.067) (0.045) (0.044) (0.056) Married, not living with spouse -0.203 *** -0.091 * -0.014 0.052 (0.061) (0.049) (0.052) (0.068) Never married 0.034 0.014 0.042 -0.008 (0.033) (0.024) (0.026) (0.035) Divorced -0.079 *** 0.009 0.107 *** 0.085 *** (0.027) (0.02) (0.021) (0.027) Widowed 0.024 0.015 0.019 -0.015 (0.022) (0.018) (0.021) (0.029) Job Situation: Retired Reference group Employed 0.374 *** 0.251 *** 0.206 *** 0.188 *** (0.029) (0.019) (0.018) (0.024) Unemployed -0.188 *** -0.169 *** -0.221 *** -0.128 ** (0.046) (0.034) (0.038) (0.053) Permanently sick -1.162 *** -1.262 *** -1.245 *** -0.923 *** (0.032) (0.033) (0.054) (0.08) Home-maker -0.062 ** -0.021 -0.081 *** -0.069 ** (0.027) (0.021) (0.024) (0.034) Other -0.317 *** -0.152 *** -0.148 *** -0.017 (0.041) (0.037) (0.046) (0.064) ***: 1% significant; 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